MOVABLE OBJECT STATUS DETERMINATION

• US 20100316257A1
• Filed: 02/19/2009
• Published: 12/16/2010
• Est. Priority Date: 02/19/2008
• Status: Abandoned Application

• Current Assignee: British Telecommunications PLC (BT Group PLC)
• Original Assignee: British Telecommunications PLC (BT Group PLC)
• Inventors: Xu, Li-Qun, Anjulan, Arasanathan
• Application Number:
  US12/918,439
• Publication Number:
  US 20100316257A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• 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 2201/08 Detecting or categorising vehicles

Abstract

Embodiments of the present invention relate to automated methods and systems for determining a degree of presence of a movable object in a physical space. Video images are used to define a region of interest (1305) in the space and partition the region of interest into an array of sub-regions (1310). Then, first and second spatial-temporal visual features are determined, and metrics are computed (1320), (1340), to characterise whether or not each sub-region contains a moving or stationary object. The metrics are used to generate (1350) an indication of the overall degree of presence within the region of interest.

Claims

1. A method of determining a status of a movable object in a physical space by automated processing of a video sequence of the space, the method comprising:

determining a region of interest accommodating a pre-determined path of the object in the space;

partitioning the region of interest into an array of sub-regions;

determining first spatial-temporal visual features within the region of interest and, for one or more sub-regions, computing a metric based on the said features indicating whether or not a said object is moving in the sub-region;

determining second spatial-temporal visual features within the region of interest and, for one or more sub-regions, computing a metric based on the said features indicating whether or not a said object is stationary in the sub-region;

generating an overall degree of presence for an object in the region of interest on the basis of both moving and stationary metrics.

2. A method according to claim 1, wherein second spatial-temporal features are determined only for sub-regions that do not have an object moving therein.

3. A method according to claim 1, wherein partitioning the region of interest includes defining each sub-region so that it has an area within an upper and lower bound.

4. A method according to claim 1, wherein the sub-regions have a maximum size of 2500 pixels and a minimum size of 100 pixels.

5. A method according to claim 1, wherein the sub-regions have a maximum size of 2000 pixels and a minimum size of 250 pixels.

6. A method according to claim 1, including assigning a weighting to each sub-region that is only partially within the region of interest.

7. A method according to claim 1, wherein object movement within a sub-region is determined including by identifying first spatial-temporal visual features indicative of greater than a threshold level of activity within a sub-region using a first adaptive background reference model and by comparing a current video image witĥ

a previous video image.

8. A method according to claim 7, wherein object movement within a sub-region is determined by comparing a current image with a previous image in order to characterise any global changes to the current image, and reducing the influence of any identified first spatial-temporal visual features that result from any such global changes in the image.

9. A method according to claim 1, wherein a stationary object within a sub-region is determined including by identifying second spatial-temporal visual features indicative of greater than a threshold level of difference between a sub-region of a current video image and the same sub-region of a second adaptive background reference model.

10. A method according to claim 9, wherein a stationary object within a sub-region is determined including by comparing a current image with a second adaptive background reference model in order to characterise any global changes to the current image, and reducing the influence of any identified second spatial-temporal visual features that result from any such global changes in the image.

11. A method according to claim 10, wherein the first adaptive background reference model is a relatively short term responsive background model and the second adaptive background reference model is a relatively long term stationary background model.

12. A method according to claim 1, in which the physical space includes a train platform, the object is a train and the region of interest is a region of video image through which the train travels or rests when entering, waiting and/or leaving the platform.

13. A method according to claim 1, including determining crowd congestion in said physical space by:

determining a second region of interest in the space;

partitioning the second region of interest into an irregular array of sub-regions, each comprising a plurality of pixels of video image data;

assigning a congestion contributor to each sub-region in the irregular array of sub-regions;

determining first spatial-temporal visual features within the region of interest and, for at least one sub-region, computing a metric based on the said features indicating whether or not the sub-region is dynamically congested;

determining second spatial-temporal visual features within the region of interest and, for at least one sub-region, computing a metric based on the said features indicating whether or not the sub-region is statically congested;

generating an indication of an overall measure of congestion for the second region of interest on the basis of both dynamically and statically congested sub-regions and their respective congestion contributors.

14. A system determining a degree of presence of a movable object in a physical space by automated processing of a video sequence of the space, the system comprising:

an imaging device for generating images of a physical space; and

a processor,

wherein, for a given region of interest in images of the space, the processor is arranged to;

partition the region of interest into an array of sub-regions;

determine first spatial-temporal visual features within the region of interest and, for one or more sub-regions, computing a metric based on the said features indicating whether or not a said object is moving in 5 the sub-region;

determine second spatial-temporal visual features within the region of interest and, for one or more sub-regions, computing a metric based on the said features indicating whether or not a said object is stationary in the sub-region;

generate an overall degree of presence for an object in the region of interest on the basis of both moving and stationary metrics.