US20070058842A1

US20070058842A1 - Storage of video analysis data for real-time alerting and forensic analysis

Info

Publication number: US20070058842A1
Application number: US11/520,532
Authority: US
Inventors: Robert Vallone; Stephen Fleischer; Colvin Pitts; Gordon Haupt; Timothy Frederick; Thomas Faulhaber; Marcus Marinelli
Original assignee: Individual
Current assignee: 3VR Security Inc
Priority date: 2005-09-12
Filing date: 2006-09-12
Publication date: 2007-03-15
Also published as: US20140022387A1; WO2007033352A3; US8553084B2; WO2007033352A2; US20070061696A1; KR20080075091A; US9224047B2; WO2007033351A2; WO2007033351A3

Abstract

A method and apparatus for storing video data is provided. Video data, that comprises a series of frames, is received. Information about changes that are detected in the series of frames is generated. The information is aggregated to generate a plurality of video data change records. Each video data change record corresponds to a plurality of frames and includes change information that indicates changes that were detected relative to the corresponding plurality of frames. Events of interest that satisfy specified search criteria are searched for by comparing the specified search criteria against change information in one or more of the plurality of video data change records.

Description

CLAIM OF PRIORITY

This application claims priority to U.S. Provisional Patent Application Ser. No. 60/716,729 filed Sep. 12, 2005, the contents of which is incorporated herein in its entirety for all purposes.

FIELD OF THE INVENTION

The present invention relates to efficiently storing video data, and more specifically, storing information about visual changes that has been aggregated over a series of frames of the video data.

BACKGROUND

Analyzing video streams to determine whether or not any interesting activities or objects are present is a resource-intensive operation. Software applications are used to analyze video data, attempting to recognize certain activities or objects in the video data. For example, recognition applications exist for recognizing faces, gestures, vehicles, guns, motion, and the like. Often, such applications are used to analyze surveillance video streams for security purposes.
If a user is interested in whether a particular object (e.g. face or gun) appears in a video stream, a software application may be used to detect the particular object, and store data that records that the object was detected. Typically, the amount of storage space needed to record the detection of those objects is relatively small. However, under some circumstances, one may not know ahead-of-time what events of interest will occur in a video stream. In such cases, one could theoretically try to detect and capture all possible changes that occur within the video stream. However, doing so would require a prohibitively large amount of storage space. Not only would storage capacity issues arise from storing all possible change information, but it would be difficult to perform searches against such a vast amount of information.
Due to the impracticality of such an all-changes storage technique, current approaches for scanning for suspicious behavior captured in video necessarily employ human involvement. Not only is significant human involvement prohibitively expensive (especially for small to mid-size businesses), people are prone to error. Watching hours of live or recorded video is extremely fatiguing, which fatigue may result in missing suspicious activity. Furthermore, a computer may operate continuously whereas people require sleep and rest.
Based on the foregoing, there is a need for efficiently storing motion and other change information to reduce the amount of data stored and to increase search speed.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:
FIG. 1 is a flow diagram that illustrates how video data may be stored, according to an embodiment of the invention;
FIG. 2 is a block diagram that illustrates how video data change records may represent varying amounts of video data, according to an embodiment of the invention;
FIG. 3 is a graphical depiction that illustrates how change information may be stored on a per-region basis, according to an embodiment of the invention;
FIG. 4 is a block diagram that illustrates how video data change records may store specific and generalized change information, according to an embodiment of the invention; and
FIG. 5 is a block diagram of a computer system on which embodiments of the invention may be implemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

General Overview

In order to (1) store a minimal amount of video data and (2) quickly search video data for certain events, an efficient storage mechanism is proposed. Instead of storing information related to changes detected in video data on a frame-by-frame basis, the information is aggregated across all or most of the corresponding frames and stored as a single logical record in a storage system. For example, typical video cameras and display devices operate at approximately 24 video frames per second. If motion is detected within a particular view and the motion lasts for one minute, then instead of storing 1440 different records to represent the motion, the motion information is stored in a single record that represents the 1440 frames corresponding to the motion.
Because the amount of information that is stored is relatively small, the searches for visual changes that satisfy certain criteria can also be performed very efficiently. For example, if a user desired to search for a certain type of motion in 1440 frames, then only one record would have to be searched, as opposed to 1440 records.
The embodiments of the invention described herein are illustrated in the context of video surveillance systems. However, embodiments of the invention are limited to that context. Embodiments of the invention are also relevant in other non-surveillance contexts, such as searching for certain motion patterns in a series of computer-generated frames.

Functional Overview

FIG. 1 is a flow diagram that illustrates how video data may be stored and used to search for changes detected in the video data, according to an embodiment of the invention. In step 102, video data that comprises a series of frames is received. In step 104, information about visual changes that are detected in the series of frames is generated. In step 106, the generated information is aggregated to generate a plurality of video data change records (VDCRs). Each video data change record corresponds to a plurality of frames and includes change information that indicates visual changes that were detected relative to the corresponding plurality of frames.
In step 108, events of interest that satisfy specified search criteria are searched for by comparing the specified search criteria against change information in one or more of the plurality of visual change records. As shall be described in greater detail hereafter, changes detected in the same sequence of video frames may be aggregated at multiple levels of granularity. For example, in the spatial dimension, the changes may be aggregated at the entire-view level, the quadrant level, and the grid-point level. Similarly, in the temporal dimension, the changes may be aggregated at per-week, per-day, per-hour, per-minute and per-second levels of granularity, or at variable time intervals that depend on other criteria.

Definitions

A “video data change record” (VDCR) is a logical composition of one or more fields, items, attributes, and/or objects. A VDCR corresponds to a plurality of frames and includes change information (described below). A VDCR corresponds to a particular level of temporal and spatial granularity. A VDCR may contain information about one or more events that were detected in the video data within the spatial/temporal space associated with the VDCR. VDCRs may also store change information pertaining about other events that do not appear in the frames that correspond to the VDCR. For example, a VDCR may store information indicating that an audible alarm began ringing during the time interval associated with the VDCR, even though there is no indication within the video stream of the alarm.
A VDCR may also include, but is not limited to, (a) a start time of when the first frame in the plurality of frames was captured, (b) an end time of when the last frame in the plurality of frames was captured, (c) a time duration indicating the difference between the start time and the end time, (d) type data indicating whether the change corresponds to a detection of motion or only a pixel change, (e) shape data indicating a shape (e.g., person, car) of a moving object that triggered the VDCR, (f) behavior data indicating a behavior (e.g., walking, running, driving) of a moving object that triggered the VDCR, and (g) an indication of whether the VDCR corresponds to an event or a specified time interval.
A VDCR may also contain a reference to the actual video data that corresponds to the plurality of frames of the VDCR in order to enable a user of the storage system to view the corresponding video data. If a VDCR contains a start time, then the start time may be used as the reference.
“Change information” is information that indicates visual changes that are detected in the temporal/spatial interval associated with a VDCR. In one embodiment, the change information for changes associated with a VDCR is stored in the VDCR. Change information may indicate motion that is detected in the plurality of frames and/or a change in pixel values that is detected in the plurality of frames, such as brightness and hue. For example, a pixel change may result from the shadow, of a person, that enters and leaves a view represented by the frames. A pixel change may also result from a light bulb turning on or off that affects the brightness of objects in the frames. In some instances, the last frame in an event may appear as an exact duplicate of the first frame of the event. For example, suppose a light bulb faded out and then back on. By simply differencing the pixel values of the first frame with the pixel values of the last frame, the difference may be zero. Thus, the change information may indicate the greatest amount of change. For example, if the light bulb mentioned above went out and then back on and the possible pixel values range from 0-100, the change information may indicate 100 instead of zero.
Correspondingly, if the change information indicates a motion, then the change information may further indicate all directions and/or speeds of the motion. For example, with a particular view, an object may move right, left, up, and down. Thus, the change information may indicate all directions. As another example, if the object moved at five different speeds in a certain direction, then change information may indicate the largest speed.
Any method for detecting and calculating visual changes (whether just pixel change or motion) may be used. Thus, embodiments of the invention are not limited to any particular method.
Change information may further include information on a per-region basis. A “region” is a portion of a two-dimensional view (e.g., captured by a video camera) of the video data. The view may be divided into multiple uniform regions, such as in a grid layout. However, a region may be of any arbitrary size and shape. Thus, change information may include motion and/or pixel change information for each specified region of the view for the duration of the plurality of frames that corresponds to the change information.
An “event” is generally associated with a visual change detected in video data. For example, an event may correspond to the detection of a person walking in a region of the view. The duration of the event is typically the length of time that the visual change occurs. Once no more visual change is detected, then the event may end.
An event may be initiated, not only on the detection of visual changes within a view, but also upon the occurrence of an external event. For example, an event may be triggered by a fire alarm. Once the fire alarm is detected, the frames of video data from that point on are used to generate a VDCR that represents the event. The event may end, for example, when the fire alarm ends or when an administrator of a video surveillance system indicates that the event is completed.
Alternatively, a VDCR may correspond to a specified time interval instead of to an event. For example, regardless of whether a visual change is detected, a VDCR may be generated for each 5-minute interval after every hour. As another example, a VDCR may be generated for each 24 hour period.
A VDCR may be generated from other VDCRs and not necessarily from the video data itself. For example, if a VDCR is generated for each one-hour period of each day, then a “day” VDCR may be generated directly from the twenty-four “hour” VDCRs that correspond to that day. Similarly, a “week” VDCR may be generated from seven “day” VDCRs, and so forth.
Similarly, a view-level VDCR may be generated based on the change information in the corresponding quadrant VDCRs, and the quadrant VDCRs may be generated based on the change information in the corresponding region VDCRs.

Storage of Visual Changes

Because a single VDCR may correspond to thousands or millions of frame of video data, the storage space required to store visual changes is much smaller than is required otherwise (e.g., storing a VDCR for each two-frame sequence where motion is detected).
FIG. 2 is a block diagram that illustrates how video data change records may represent varying amounts of video data, according to an embodiment of the invention. Video data 201 comprises a series of frames. Each block of video data 201 may represent, for example, 100 frames. Thus, VDCR 202 represents 400 frames and VDCR 206 represents 600 frames. VDCRs 202-226 are comprised of two sets: a VDCR set 230 and a VDCR set 240. Suppose that each VDCR in VDCR set 230 (i.e., VDCRs 202-214) is generated based on events, as opposed to a pre-specified time interval regardless of visual changes. For example, VDCR 204 represents an event that lasted 600 frames, whereas VDCR 206 represents an event that lasted 200 frames.
Further suppose that each VDCR in VDCR set 240 (i.e., VDCRs 222-226) is generated for each hour. Thus, for example, VDCR 222 may represent hour # 1, VDCR 224 may represent hour #2, and VDCR 226 may represent hour #3. Therefore, a specific period of time may be represented by multiple VDCRs that each represents different periods of time.
In one embodiment, if a VDCR represents a number of frames that is also represented by another VDCR, then each VDCR contains a reference to the other VDCR, as illustrated. Thus, VDCR 222 contains references to VDCRs 202 and 204 and VDCRs 202 and 204 each contain a reference to VDCR 222. Similarly, VDCR 226 contains references to VDCRs 210-214 and VDCRs 210-214 each contain a reference to VDCR 226.
VDCRs may be stored on disk in specified tables. Each table may correspond to VDCRs of a certain type. For example, each table of a plurality of tables may comprise VDCRs that correspond to a specified time interval (e.g., day table, week table, month table, etc.). As another example, each table of a plurality of tables may comprise VDCRs that correspond to certain time frames (e.g., all VDCRs generated in January, 2006, or all VDCRs generated during week #51, etc.). Embodiments of the invention are not limited to how VDCRs are organized on disk.

Regions

As described above, a two-dimensional view of video data may be divided into multiple regions. A region may or not be convex. Multiple regions within a view may be of different sizes and shapes.
If a VDCR is generated based on a detected change in pixel values that is not associated with motion of an object, then the change information of a particular region may indicate the amount of change in pixel values within that particular region.
If a VDCR is generated based on a motion event, then the change information of a particular region may indicate the direction and/or velocity of the detected motion within that particular region. For example, suppose a ball was thrown through the view of a camera. A VDCR was generated for the few frames that captured the event. The change information in every region of the view through which the ball traveled will indicate that motion was detected in that region and may indicate the direction of the ball and the velocity of the ball.
FIG. 3 is a graphical depiction that illustrates how change information may be stored on a per-region basis, according to an embodiment of the invention. In this example, a camera view is divided into multiple, non-overlapping rectangle regions. Each region in FIG. 3 indicates one or more directions of motion. For example, region 302 indicates that at least four directions of a motion have been detected for that particular region. As this example further illustrates, not every region must specify a direction and/or speed, such as region 304. In such a case, the change information for such a region may be empty or include some indicator indicating zero direction and/or speed.

Abstraction of Change Information

In one embodiment, change information may be kept, not only in per-region VDCRs, but also in multi-region VDCRs. For example, if there are 100 regions within a view, and change information is maintained for each region for a given region-level VDCR, then view-level VDCRs may indicate change information for the entire view, quadrant-level VDCRs may indicate change information for each quadrant of the view, etc. In each VDCR, the change information is abstracted to the level of the VDCR. Thus, change information for a view-level VDCR may contain 1/00^ththe information of the information contained in the corresponding 100 region-level VDCRs. Similarly, the change information for a single quadrant-level VDCR may contain 1/25^ththe information of the information contained in the corresponding twenty-five region-level VDCRs that correspond to the quadrant.
It should be noted that VDCRs are logical pieces of information, and do not necessarily correspond to distinct records within a repository. For example, a single record or data structure within a repository may include a view-level VDCR, its corresponding quadrant-level VDCRs, and its corresponding region-level VDCRs. Similarly, a single record or data structure may be used to store change information aggregated at the week level, the day, level, the hour level and the minute level. Data structures that store change data that has been aggregated at multiple levels of granularity are referred to herein as composite VDCRs.
The nature of the repository used to store VDCRs may vary from implementation to implementation. The techniques described herein are not limited to any particular type of repository. For example, the VDCRs may be stores in a multi-dimensional database, a relational database, or an object-relational database. In one embodiment, separate relational tables are used to store VDCRs at different levels of granularity. Thus, one relational table may have rows that correspond to view-level VDCRs, while another relational table may have rows that correspond to region-level VDCRs. In such an embodiment, indexes may be used to efficiently locate the region-level VDCRs that correspond to a particular view-level VDCR.
FIG. 4 is a block diagram that illustrates how composite video data change records may store specific and generalized change information, according to an embodiment of the invention. Video data 401 comprises a series of frames. Each block of video data 401 may represent any number of frames, such as one hundred. Thus, composite VDCR 402 may represent four hundred frames. The change information of VDCR 402 may be represented on a per-region basis, a per-quadrant basis, a per-view basis, and/or any other basis. In this example, VDCR 402 comprises view data 403, quadrant data 404, and region data 405. VDCR 402 may comprise other information such as whether any visual change was detected in the frames that correspond to VDCR 402 and the type of visual change (e.g., pixel change, motion).

Alerts

In one embodiment, search criteria may be specified in which all incoming video data is analyzed to determine whether any detected visual changes satisfy the search criteria. Thus, for an ongoing event (i.e., before a VDCR is generated for the event), an alert may be triggered on the precise frame that the change information (accumulated thus far) first satisfied the search criteria. Once the event has completed, the accumulated change information is stored (in the manner described above) in a VDCR so that future searches with similar search criteria may return that VDCR.

Filters

In one embodiment, several levels of filtering may be performed for various reasons, some of which may include (1) reducing noise that may be generated when generating change information and (2) determining dominant motion areas and velocities within a scene. The change information of a particular VDCR may be filtered across adjacent regions within a frame, or across frames, of the corresponding plurality of frames and adjacent frames within the corresponding plurality of frames, using various methods that include, but are not limited to, smoothing filters, median filters, and multi-dimensional clustering algorithms.

Searching

With the storage techniques described above, the generated VDCRs facilitate fast searches across multiple events and specified time intervals. Because searches are executed against change information, as described above (which may be thought of as meta-data about visual changes), rather than the entire video data itself, the searches may be performed much faster than if a user was required to search through the entire video data or search on a frame-by-frame basis of each detected change.
Thus, a user may specify search criteria that are compared against each VDCR. For example, a user may search for all VDCRs that indicate any motion, where the motion is more than 20 mph. As another example, a user may search for one VDCR that indicates a pixel change in the lower left quadrant of a 50% change in brightness.
Multiple indexes may be generated in order to facilitate faster searches. Such indexes may be based on time, the type of visual change, the speed of a motion, the direction of a motion, etc.
Furthermore, the manner in which change information is stored and the varying types of information that a VDCR may include make possible many types of searches. For example, search criteria of a particular search may include (1) multiple ranges of time, (2) the speed of motion in some regions, (3) the direction of motion in other regions, (4) an amount of pixel change in still other regions, (5) the shape and type of behavior of multiple detected objects, etc. The number of possible search criteria is immeasurable.

Multi-Level Searching—Regions

As described above, change information that is generated from video data may be aggregated at different levels of spatial granularity. For example, the change information stored for a particular time period may include (1) view-level VDCRs that indicate change information relative to the entire view, (2) quadrant-level VDCRs that indicate change information for each of four quadrants of the view, and (3) region-level VDCRs that indicate change information for each of a thousand grid regions within the view. The search mechanism may make use of these different levels of granularity to improve search performance.
For example, suppose a view is divided into one hundred non-overlapping regions. Further, suppose that a user is searching for motion events that occurred over a particular week, and that a million region-level VDCRs have been generated for each region during that week. Suppose that the search criteria includes that a specified type of motion occurred within each region of twenty-four specified regions of the view. In this example, if the entire search is performed at the region-level of granularity, then twenty-four million region-level VDCRs will have to be inspected during the search.
Instead of performing the entire search at the region-level of granularity, a multi-level search may be performed. Specifically, during the first phase of the multi-level search, each of a million view-level VDCRs may be inspected to find those view-level VDCRs that indicate that the specified motion occurred anywhere within the view. The determination may be based on view-level change information in each view-level VDCR. The view-level change information of a view-level VDCR indicates whether motion was detected anywhere in the entire view during the frames associated with the view-level VDCR. In the present example, the first-level search will involve one million comparisons (one for each view-level VDCR). For the purpose of explanation, assume that 50,000 view-level VDCRs matched the first-level search.
During the second-phase of the multi-level search, quadrant-level VDCRs are inspected. However, rather than inspecting all 4 million of the quadrant-level VDCRs, only the quadrant-level VDCRs that correspond to the 50,000 view-level VDCRs are searched in the second-level search. Further, if the 24 regions specified in the search criteria only fall within two of the four quadrants, then the second-level search need only involve the quadrant-level VDCRs associated with those two quadrants. Thus, the second phase of the search will involve no more than 100,000 quadrant-level VDCRs.
Each quadrant-level VDCR includes quadrant-level data that indicates whether motion was detected in any portion of the corresponding quadrant. For the purpose of explanation, assume that, based on the quadrant-level VDCRs, only 10,000 view-level VDCRs of the 50,000 VDCRs included motion in those two quadrants.
In the third level search, a region-level search is performed against the region-level VDCRs that correspond to the 10,000 view-level VDCRs. When searching at the region-level of granularity, 24 region-level VDCRs may need to be inspected for each of the 10,000 view-level VDCRs. However, because the candidate set of view-level VDCRs has been pruned down during the first two search phases, the number of region-level comparisons performed during the third-level search (240,000, in the present example) will typically be far fewer than the number of comparisons (24 million) that would have been performed if all searching was done at the region-level of granularity.

Multi-Level Searching—Time

As with areas of a view, a search may be separated into a multi-level search according to time. For example, suppose a user wants to find motion events that occurred between the hours of 1:00 AM and 5:00 AM during the past week. Further suppose that an hour-level VDCR exists for each hour and each day. Thus, in the first search level, each day-level VDCR of the past week is examined to determine whether motion was detected in the corresponding day. In the second search level, each hour-level VDCR that is associated with a day-level VDCR that was identified in the first search level is examined to determine whether motion was detected in the corresponding hour.
In one embodiment, one level of a multi-level search may be performed based on time and another level of the multi-level search may be performed based on areas of the view. For example, suppose search criteria specifies motion that occurred within a certain area of a view between the hours of 1:00 AM and 5:00 AM during the past week. Thus, the first two levels of the search may be used to identify all hour-level/view-level VDCRs of the past week between 1:00 AM and 5:00 AM. Subsequent levels of the search may be used to identify all hour-level/region-level VDCRs with change information that indicates the specified motion in the specified area.
In one embodiment, users may specify the search criteria for each level of a multi-level search. In another embodiment, multi-level searches may be performed automatically transparent to the user, beginning at relatively coarser temporal/spatial granularities and ending at the level of granularities of the search criteria that was specified by the user. Thus, a single set of search criteria may be automatically divided (e.g., by a query compiler) into one or more general searches and one specific search. Any mechanism for dividing search criteria into a multi-level query may be used. Embodiments of the invention are not limited to any specific mechanism.

Hardware Overview

FIG. 5 is a block diagram that illustrates a computer system 500 upon which an embodiment of the invention may be implemented. Computer system 500 includes a bus 502 or other communication mechanism for communicating information, and a processor 504 coupled with bus 502 for processing information. Computer system 500 also includes a main memory 506, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 502 for storing information and instructions to be executed by processor 504. Main memory 506 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 504. Computer system 500 further includes a read only memory (ROM) 508 or other static storage device coupled to bus 502 for storing static information and instructions for processor 504. A storage device 510, such as a magnetic disk or optical disk, is provided and coupled to bus 502 for storing information and instructions.
Computer system 500 may be coupled via bus 502 to a display 512, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device 514, including alphanumeric and other keys, is coupled to bus 502 for communicating information and command selections to processor 504. Another type of user input device is cursor control 516, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 504 and for controlling cursor movement on display 512. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
The invention is related to the use of computer system 500 for implementing the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system 500 in response to processor 504 executing one or more sequences of one or more instructions contained in main memory 506. Such instructions may be read into main memory 506 from another machine-readable medium, such as storage device 510. Execution of the sequences of instructions contained in main memory 506 causes processor 504 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software.
The term “machine-readable medium” as used herein refers to any medium that participates in providing data that causes a machine to operation in a specific fashion. In an embodiment implemented using computer system 500, various machine-readable media are involved, for example, in providing instructions to processor 504 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 510. Volatile media includes dynamic memory, such as main memory 506. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 502. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. All such media must be tangible to enable the instructions carried by the media to be detected by a physical mechanism that reads the instructions into a machine.
Common forms of machine-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punchcards, papertape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
Various forms of machine-readable media may be involved in carrying one or more sequences of one or more instructions to processor 504 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 500 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 502. Bus 502 carries the data to main memory 506, from which processor 504 retrieves and executes the instructions. The instructions received by main memory 506 may optionally be stored on storage device 510 either before or after execution by processor 504.
Computer system 500 also includes a communication interface 518 coupled to bus 502. Communication interface 518 provides a two-way data communication coupling to a network link 520 that is connected to a local network 522. For example, communication interface 518 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 518 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 518 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
Network link 520 typically provides data communication through one or more networks to other data devices. For example, network link 520 may provide a connection through local network 522 to a host computer 524 or to data equipment operated by an Internet Service Provider (ISP) 526. ISP 526 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 528. Local network 522 and Internet 528 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 520 and through communication interface 518, which carry the digital data to and from computer system 500, are exemplary forms of carrier waves transporting the information.
Computer system 500 can send messages and receive data, including program code, through the network(s), network link 520 and communication interface 518. In the Internet example, a server 530 might transmit a requested code for an application program through Internet 528, ISP 526, local network 522 and communication interface 518.
The received code may be executed by processor 504 as it is received, and/or stored in storage device 510, or other non-volatile storage for later execution. In this manner, computer system 500 may obtain application code in the form of a carrier wave.
In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. Thus, the sole and exclusive indicator of what is the invention, and is intended by the applicants to be the invention, is the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. Hence, no limitation, element, property, feature, advantage or attribute that is not expressly recited in a claim should limit the scope of such claim in any way. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

Claims

1. A machine-implemented method, comprising:

generating information about changes that are detected in video data that includes a series of frames;

aggregating the information to generate a plurality of video data change records, wherein each video data change record corresponds to a plurality of frames and includes change information that indicates changes that were detected in video data relative to the corresponding plurality of frames; and

storing said aggregated video data change records for subsequent forensic analysis.

2. The method of claim 1, wherein:

the plurality of video data change records includes a particular video data change record that corresponds to an event detected within the video data; and

the change information contained in the particular video data change record indicates changes that occurred in the frames that correspond to said event.

3. The method of claim 1, wherein:

the plurality of video data change records includes a set of video data change records that each corresponds to a specified time interval; and

the change information contained in each video data change record of the set of video data change records indicates changes that occurred in the frames that correspond to the corresponding specified time interval.

4. The method of claim 1, wherein the change information contained in each video data change record of the plurality of video data change records includes change type information that indicates at least one of:

(a) a change in pixel values that occurred during the plurality of frames that correspond to said each video data change record, and

(b) a detection of motion of one or more objects in the plurality of frames that correspond to said each video data change record.

5. The method of claim 4, wherein:

the change type information of each video data change record indicates the detection of motion of one or more objects captured in the plurality of frames that correspond to said each video data change record; and

said each video data change record further indicates at least one of a direction and a speed of said motion.

6. The method of claim 1, wherein:

said plurality of video data change records includes a plurality of region change records; and

each region change record of the plurality of region change records corresponds to a separate region of a frame and contains region change information about the changes that were detected in the separate region relative to the plurality of frames that correspond to the particular video data change record.

7. The method of claim 6, wherein:

the region change information of said each region change record is filtered based on other region change information of other region change records of the plurality of region change records; and

the other region change records correspond to other regions, of the frame, that are adjacent to the region that corresponds to said each region change record.

8. The method of claim 1, wherein the change information of a particular video data change record of the plurality of video data change records is filtered, based on the plurality of frames that correspond to said particular video data change record.

9. The method of claim 1, further comprising searching for events of interest that satisfy specified search criteria by comparing the specified search criteria against change information in one or more of said plurality of video data change records.

10. The method of claim 6, wherein:

the method further comprises searching for events of interest that satisfy specified search criteria by comparing the specified search criteria against change information in one or more of said plurality of video data change records;

said specified search criteria indicates a first region of a set of frames in which the events of interest must have occurred;

searching for the events of interest that satisfy the specified search criteria includes:

identifying a first set of video data change records that correspond to an area of the view that includes said first region and is larger than said first region; and

based on the first set of video data change records, identifying a second set of video data change records that correspond to said first region, wherein the change information of each video data change record in the second set satisfies the specified search criteria.

11. The method of claim 1, wherein:

said specified search criteria specifies time at which the events of interest must have occurred;

identifying a first set of video data change records that correspond to a first time interval that includes said specified time; and

based on said first set of video data change records, identifying a second set of video data change records that aggregate change information at a finer level of granularity than the video data change records in said first set of video data change records;

wherein the change information of each video data change record in the second set includes said specified time and satisfies the specified search criteria.

12. The method of claim 1, wherein at least one video data change record of said plurality of video data change records corresponds to a time interval that was established, at least in part, by an event that is not reflected in said video data.

13. The method of claim 9, further comprising:

receiving second specified search criteria;

detecting an event in said video data as the video data is being received;

generating aggregated change information associated with said event;

before a video data change record is generated for said event, comparing said second specified search criteria against said aggregated change information; and

if said aggregated change information satisfies said second specified search criteria, immediately generating an alert that indicates that said second specified search criteria has been satisfied.

14. A machine-implemented method, comprising:

generating information about changes that are detected in video data that includes a series of frames; and

aggregating the information to generate a first plurality of video data change records, wherein each video data change record in the first plurality of change records corresponds to a plurality of frames and includes change information, aggregated at a relatively fine level of granularity, that indicates changes that were detected relative to the corresponding plurality of frames; and

aggregating the information to generate a second plurality of video data change records, wherein each video data change record in the second plurality of change records corresponds to a plurality of frames and includes change information, aggregated at a relatively coarse level of granularity, that indicates changes that were detected relative to the corresponding plurality of frames.

15. The method of claim 14 wherein:

each of the first plurality of video data change records corresponds to regions of a view; and

each of the second plurality of video data change records corresponds to the entire view.

16. The method of claim 14 wherein:

each of the first plurality of video data change records corresponds to time intervals of a first duration; and

each of the second plurality of video data change records corresponds to time intervals of a second duration, wherein the second duration is longer than said first duration.

17. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 1.

18. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 2.

19. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 3.

20. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 4.

21. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 5.

22. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 6.

23. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 7.

24. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 8.

25. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 9.

26. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 10.

27. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 11.

28. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 12.

29. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 13.

30. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 14.

31. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 15.

32. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 16.