Video motion anomaly detector

• US 20060109341A1
• Filed: 07/09/2003
• Published: 05/25/2006
• Est. Priority Date: 08/15/2002
• Status: Active Grant

• Current Assignee: ROKE Manor Research Limited (Chemring Group PLC)
• Original Assignee: ROKE Manor Research Limited (Chemring Group PLC)
• Inventors: Evans, Richard John
• Granted Patent: US 7,864,980 B2
• Time in Patent Office: Days
• US Class Current: 348/14.80
• CPC Class Codes: G01S 3/7864 T.V. type tracking systems
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• 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: G08B 21/0423 detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 21/0476 Cameras to detect unsafe condition, e.g. video cameras

Abstract

The Video Motion Anomaly Detector addresses the problem of automatically detecting events of interest to operators of CCTV systems used in security, transport and other applications, processing CCTV images. The detector may be used in a number of ways, for example to raise an alarm, summoning a human operator to view video data, or to trigger selective recording of video data or to insert an index mark in recordings of video data.

Claims

1. A method for processing video images to detect an event of interest, comprising the steps of:

receiving a video signal representing the video images to be processed;

extracting at least one point feature from the video signal;

tracking the position and movement of the at least one point feature within the video images to generate a corresponding at least one track, each representing a corresponding point feature;

using an iterative learning process to derive a normal pattern of behavior for each track;

comparing present behavior of the at least one track to the respective normal pattern of behavior; and

in response to the present behavior falling outside the normal pattern of behavior, generating an alarm signal.

2. A method according to claim 1, wherein the alarm signal causes at least one of the following effects:

draw the attention of an operator;

place an index mark at the appropriate place in recorded video data; and

trigger selective recording of video data.

3. A method according to claim 1, wherein the learning process accumulates data representing the behavior of the track(s) over a period of time in a four-dimensional histogram, said four dimensions representing x-position, y-position, x-velocity and y-velocity, of the track(s) within the video image.

4. A method according to claim 3, wherein the learn behavior stage segregates the tracks according to a velocity threshold;

wherein tracks moving at a velocity below the velocity threshold are considered stationary while tracks moving at a velocity in excess of the velocity threshold are considered mobile;

wherein data concerning the mobile tracks is stored in said four-dimensional histogram, data concerning the stationary tracks being stored in a two-dimension histogram, said two dimensions representing x-position and y-position within the video image.

5. A method according to claim 3, wherein a cell size of the four-dimensional histogram varies in accordance with a measured speed in the image of each respective track.

6. A method according to claim 3, wherein the histogram is periodically de-weighted in order to bias the result of the learning process towards more recent events.

7. A method according to claim 1, wherein the comparison process classifies a track according to a comparison of the frequency of occupation of the corresponding histogram cell with an occupancy threshold.

8. A method according to claim 7 wherein the comparison process acts to classify as normal behavior a track adjacent or near a cell which is above the occupancy threshold, despite the track appearing in a cell below the occupancy threshold, where one cell is considered to be near another if the distance between them s below a predetermined distance threshold.

9. A method according to claim 1, wherein abnormal tracks are filtered, whereby an active alarm signal is generated in response to an abnormal track which resembles a number of other abnormal tracks, in terms of at least one of position, velocity and time.

10. A method according to claim 1, wherein abnormal tracks are filtered, whereby an active alarm signal is generated in response only to an abnormal track which has been classified as abnormal on a predetermined number of occasions.

11. A method according to claim 1, wherein abnormal tracks are filtered, whereby an active alarm signal is generated in response only to a track being classified as abnormal for the first time.

12. A method according to claim 1, wherein abnormal tracks are filtered, whereby an active alarm signal is generated only in response to a filtered version of the classification rising above a predetermined threshold value.

13. A method according to claim 1, wherein subsequent active alarm signals are inhibited for a predetermined time interval after a first active alarm signal has been produced.

14. A method according to claim 1, wherein subsequent active alarm signals are inhibited if caused by an abnormal track within a predetermined distance of another track which has previously generated an alarm.

15. Apparatus for processing video images to detect an event of interest, comprising:

a source of video images, producing a video signal representing the video images to be processed;

a feature extraction device receiving the video signal and producing data representing at least one point feature detected within the image;

a feature tracking device receiving the data representing point features and producing data representing tracks, being representative of the position and speed of each respective point feature, within the image;

a learning device receiving the data representing the tracks and producing a signal representing a range of behavior considered normal by the learning device, in response to operation of a learning process on the data representing the tracks;

a classification device receiving both the signal representing the normal range of behavior of the tracks and the data representing the tracks, being adapted to compare the signal and the data and to issue a normal/abnormal signal in accordance with the outcome of such comparison; and

an alarm generation device receiving the normal/abnormal signal and generating at least one active alarm signal in response to the normal/abnormal signal indicating abnormal behavior of at least one track.

16-17. -17. (canceled)