Video Motion Detection Method and Alert Management

• US 20160042621A1
• Filed: 06/14/2015
• Published: 02/11/2016
• Est. Priority Date: 06/13/2014
• Status: Abandoned Application

• Current Assignee: Troy Allan Gutjahr, William Daylesford Hogg
• Original Assignee: Troy Allan Gutjahr, William Daylesford Hogg
• Inventors: Hogg, William Daylesford, Gutjahr, Troy Allan
• Application Number:
  US14/738,889
• Publication Number:
  US 20160042621A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2178 based on feedback of a supervisor
• CPC Class Codes: G06V 10/7784 based on feedback from supervisors
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G08B 13/19615 wherein said pattern is defined by the user
• CPC Class Codes: H04N 23/64 Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image
• CPC Class Codes: H04N 23/6811 based on the image signal
• CPC Class Codes: H04N 23/683 performed by a processor, e.g. controlling the readout of an image memory

Abstract

This invention describes a method and apparatus for security monitoring with a video camera. A mathematical model consisting of an array of cells, or learning map, is used to describe the motion of any object(s) detected by the camera. When an object(s) is detected, its positional location(s) for a period of time, or motion event, is recorded in a learning map. This learning map is then compared to a reference learning map where the camera determines whether to alert the user or not that an object of interest was detected. After viewing the video of the motion event, the user provides feedback that impacts how the reference learning map is updated by information in the motion event learning map. Through this user feedback mechanism, the camera learns to more accurately determine whether or not to alert the user about future motion events, thus reducing the number of false alarms.

Claims

1. A method of security monitoring with a video camera apparatus where a user observes a video of the detection of object(s) of interest, provides feedback to the camera based on said observations and as a result, improves the accuracy or reliability of future detections of object(s) of interest.

2. The method of claim 1, further comprising the following steps of:

detecting the presence or lack thereof of an object(s) of interest;

generating information about said object(s);

comparing said information about said object(s) with reference information;

characterizing said object(s) based on said comparisons;

determining whether to notify user or not based on said characterization;

a user observing said object(s) and further characterizing said object(s), if required;

updating said reference information with information about said object(s), if required;

enacting a course of action based on characterization of said object(s), if required.

3. The method of claim 1 or 2, wherein the characterisation of said object(s) being determined in part by its motion over a defined period of time referred to as a motion event.

4. The method of claim 1, 2 or 3, wherein said information is described by a mathematical representation referred to as said learning map.

5. The method of claim 1, 2 or 3, wherein said further characterization of the reference information improves the accuracy of determining whether to notify the user or not.

13. A learning map as recited the above claims, wherein it is also used as a reference map for describing information from multiple motion events.

14. A learning map as recited in claim 13, wherein said cells are assigned specific weightings based on the object(s)'"'"'s motion.

15. A learning map as recited in claim 13, wherein information from a learning map described in claim 9 is used to describe a property line or horizon.

16. A learning map as recited in claim 9 or 13, wherein said cells are assigned a value corresponding to the frequency of swaying of object(s) at that location.

17. A learning map as recited in claim 9 or 13, wherein said cells are assigned a value describing the apparent size of object(s) at that location.

18. A learning map as recited in claim 9 or 13, wherein a plurality of information as described in claim 14, 15, 16 or 17 may be incorporated in a reference learning map.

6. A mathematical representation or model of a camera'"'"'s field of view suitable for describing the presence and motion of object(s) over a period of time.

7. A mathematical representation or model recited in claim 6, wherein multiple instances of said models describing multiple periods of time may be summarized to describe the presence and motion of object(s) for all instances.

8. A mathematical representation or model recited in claim 6 or 7, wherein said mathematical representation or model is referred to as a learning map, comprising:

a plurality of cells, each which may contain information;

the cells arranged in an array of rows and columns;

the array being spatially aligned with the camera'"'"'s video image field of view;

the array being spatially aligned with the camera'"'"'s video image processor'"'"'s frame of reference;

a one-to-one spatial mapping between said cells and pixels in said video image; and

location and size of said object(s) described by video image processor described by information in spatially corresponding said cells.

9. A learning map as recited in claim 8, wherein said object(s) presence and motion during said motion event is described by information.

10. A learning map as recited in claim 8 or 9, wherein only the lower edge of said object(s)'"'"'s size description is used to describe said object(s)'"'"'s presence in corresponding said cells.

11. A learning map as recited in claim 10, wherein only the defining lower corner of an object(s)'"'"'s description is used to record said object(s)'"'"'s presence in corresponding said cells when said object is moving at an angle near the learning map'"'"'s horizontal axis.

12. A learning map as recited in claim 8 or 9, wherein a combination of features in claims 10 and 11 are used depending on angle of motion to the learning map'"'"'s axis.

19. A method for managing motion event notifications and alerts with said security camera comprising the following steps of:

detecting the presence of object(s);

recording presence and motion of said object(s) for a period of time or motion event;

characterizing said object(s) presence and motion(s) in said motion event;

determining if the user is required to further characterize said object(s) in said motion event;

creating a notification of said motion event, if required;

assigning a priority to said notification based on characterizations of object(s) in said motion event, if required;

sending message to the user if no other outstanding messages are present if required; and

placing said notification in a queue based on its assigned priority if required.

20. The method of claim 19 further comprising the following steps of:

the user receiving said message;

the camera sending the highest priority notification to the user;

the user viewing video associated with the motion event and the notification;

the user further characterising observed video from said motion event;

information about said characterization being sent from the user to the camera;

the camera updating reference information based on said characterisation;

the camera re-analyzing outstanding motion events in notification queue;

the camera removing or changing priority of notifications in the queue based on said updated reference information; and

the camera sending the user a message if any outstanding notifications are in the queue.