Video camera having relational video database with analytics-produced metadata

• US 8,736,701 B2
• Filed: 11/05/2010
• Issued: 05/27/2014
• Est. Priority Date: 03/03/2008
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: VideoIQ, Inc. (Motorola Solutions, Inc.)
• Inventors: Marman, Douglas H., Saptharishi, Mahesh
• Primary Examiner(s): Ye, Lin
• Assistant Examiner(s): HOGUE, DENNIS A
• Application Number:
  US12/940,841
• Publication Number:
  US 20110050947A1
• Time in Patent Office: 1,299 Days
• Field of Search: None
• US Class Current: 348/222.1
• CPC Class Codes: H04N 23/66 Remote control of cameras or camera parts, e.g. by remote control devices
• CPC Class Codes: H04N 5/772 the recording apparatus and the television camera being placed in the same enclosure
• CPC Class Codes: H04N 9/7921 for more than one processing mode
• CPC Class Codes: H04N 9/8205 involving the multiplexing of an additional signal and the colour video signal

Abstract

Video analytics and a mass storage unit are contained in a camera housing of a video camera. The camera produces video data representing the field of view of a scene observed by the camera. The video analytics analyzes the video data and detects whether there is an occurrence of a defined feature of an object or a defined event of interest. The video analytics also generates metadata based upon the content of the video data. The video data and metadata are stored in the mass storage unit. Access to stored video data may be controlled based on content, and access to live video data may be controlled as metadata corresponding to the video data is created. The metadata can be used to search for corresponding video data representing the detected feature of an object or event of interest observed in the scene.

Claims

1. A network camera, comprising:

a camera housing;

an imaging system configured to capture a field of view and generate video of the field of view;

a time-stamp block connected to the imaging system to receive the video, and configured to attach time stamps to the video;

video analytics configured to analyze the video to detect an object or an event of interest in the video, and to generate metadata corresponding to the object or the event of interest, the video analytics operatively connected to the time-stamp block so that the metadata generated by the video analytics is, by operation of the time stamps attached to the video, synchronized to the video generated by the imaging system;

a data storage system operatively connected to the imaging system and the video analytics to receive and store the synchronized video and metadata;

a rules based engine operatively connected to the video analytics to receive the metadata, the rules based engine configured to analyze the metadata and determine actions to be taken based on rules that associate the metadata with the actions, and further configured to determine content security levels for the video by applying rules included in the rules based engine;

a storage management system operatively connected to the rules based engine and the video analytics to control storage of the video in the data storage system; and

an access control management unit configured to control access to the video stored in the data storage system according to a user security level,

wherein the metadata stored in the data storage system is searchable to allow the user to search the video stored in the data storage system and synchronized to the metadata, and the access control management unit controls access to the video stored in the data storage system such that a user is given access restricted to video having a content security level that corresponds to the user security level, and

wherein each of the imaging system, the time-stamp block, the video analytics, the data storage system, the rules based engine, the storage management system, and the access control management unit either is contained in or forms part of the camera housing.

2. The network camera of claim 1, in which the video represents a field of view of a scene under observation by the network camera, and in which the event of interest includes motion detected within the field of view of the network camera.

3. The network camera of claim 1, in which the video represents a field of view of a scene under observation by the network camera, and in which the event of interest includes motion of a blob detected within the field of view of the network camera.

4. The network camera of claim 1, in which the video represents a field of view of a scene under observation by the network camera, and in which the event of interest includes recognizing a predefined object and tracking movement of the predefined object within the field of view of the network camera.

5. The network camera claim 4, in which the video analytics uses geometric parameters to recognize the predefined object as the predefined object moves to various portions of the field of view of the network camera.

6. The network camera of claim 1, in which the object of interest is characterized by a feature that includes one of a type of the object, size of the object, speed of the object, trajectory of the object, location of the object, appearance of the object, and number count of the object.

7. The network camera of claim 1, in which the rules based engine includes rules specifying features of the object or event of interest that are detectable by the video analytics.

8. The network camera of claim 7, in which the rules included in the rules based engine are defined before the imaging system generates the video.

9. The network camera of claim 1, in which the video stored in the data storage system includes video not representing the event of interest.

10. A method of controlling a network camera having a camera housing, the network camera including an imaging system, video analytics, a rules based engine, a data storage system, and an access control management unit, each of which either is contained in or forms part of the camera housing, the method comprising:

capturing and generating video of a field of view with the imaging system;

attaching time-stamps to the video generated by the imaging system;

using the video analytics to analyze the video to detect an object or an event of interest in the video;

using the video analytics to generate metadata corresponding to the object or the event of interest, the metadata synchronized to the video by operation of the time stamps;

analyzing the metadata with the rules based engine to determine actions to be taken based on rules that associate the metadata with the actions;

determining content security levels for the video by applying rules included in the rules based engine;

storing the video and the metadata in the data storage system;

searching the video stored in the data storage system and synchronized to the metadata; and

controlling, with the access control management unit, access to the stored video in the data storage system according to a user security level, such that a user is given access restricted to stored video having the content security level that corresponds to the user security level.

11. The method of claim 10, in which the video represents a field of view of a scene under observation by the network camera, and in which the event of interest includes motion detected within the field of view of the network camera.

12. The method of claim 10, in which the video represents a field of view of a scene under observation by the network camera, and in which the event of interest includes motion of a blob detected within the field of view of the network camera.

13. The method of claim 10, in which the video represents a field of view of a scene under observation by the network camera, and in which the event of interest includes recognizing a predefined object and tracking movement of the predefined object within the field of view of the network camera.

14. The method of claim 13, in which the video analytics uses geometric parameters to recognize the predefined object as the predefined object moves to various portions of the field of view of the network camera.

15. The method of claim 10, in which the object of interest is characterized by a feature that includes one of a type of the object, size of the object, speed of the object, trajectory of the object, location of the object, appearance of the object, and number count of the object.

16. The method of claim 10, in which the rules based engine includes rules specifying features of the object or the event of interest that are detectable by the video analytics.

17. The method of claim 16, in which the rules included in the rules based engine are defined before the imaging system generates the video.

18. The method of claim 10, in which the video stored in the data storage system includes video not representing the event of interest.

19. The method of claim 10, in which:

the network camera constitutes one network camera on a distributed network surveillance system comprising multiple network cameras that each include an imaging system;

the imaging systems of the multiple network cameras generate video representing fields of view of scenes under observation by the multiple network cameras; and

the event of interest includes motion detected within one or more of the fields of view of the multiple network cameras.

20. The method of claim 10, in which:

the network camera constitutes one network camera on a distributed network surveillance system comprising multiple network cameras that each include an imaging system;

the imaging systems of the multiple network cameras generate video representing fields of view of scenes under observation by the multiple network cameras; and

the event of interest includes motion of a blob detected within one or more of the fields of view of the multiple network cameras.

21. The method of claim 10, in which:

the network camera constitutes one network camera on a distributed network surveillance system comprising multiple network cameras that each include an imaging system;

the imaging systems of the multiple network cameras generate video representing fields of view of scenes under observation by the multiple network cameras; and

the event of interest includes recognizing a predefined object and tracking movement of the predefined object within one or more of the fields of view of the multiple network cameras.

22. The method of claim 21, in which:

each of the multiple network cameras includes video analytics; and

the video analytics of the multiple network cameras use geometric parameters to recognize the predefined object as the predefined object moves within one or more of the fields of view of the multiple network cameras.

23. The method of claim 10, in which:

the network camera constitutes one network camera on a distributed network surveillance system comprising multiple network cameras that each include an imaging system;

the imaging systems of the multiple network cameras generate video representing fields of view of scenes under observation by the multiple network cameras;

each of the multiple network cameras includes video analytics;

each of the multiple network cameras includes a rules based engine; and

the rules based engines include rules specifying features of the object or event of interest that are detectable by the video analytics.

24. The method of claim 23, in which the rules included in the rules based engines are defined before the imaging systems of the multiple network cameras generate the video.