CROWD CONGESTION ANALYSIS
First Claim
1. A method of determining crowd congestion in a physical space by automated processing of a video sequence of the space, the method comprising:
- determining a region of interest in the space;
partitioning the region of interest into an irregular array of sub-regions, each comprising a plurality of pixels of video image data;
assigning a congestion weighting to each sub-region in the irregular array of sub-regions;
determining first spatial-temporal visual features within the region of interest and, for each 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 each sub-region that is not indicated as being dynamically congested, 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 region of interest on the basis of the metrics for the dynamically and statically congested sub-regions and their respective congestion weightings.
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Abstract
Embodiments of the present invention relate to automated methods and systems for analysing crowd congestion in a physical space. Video images are used to define a region of interest (205) in the space and partition the region of interest into an irregular array of sub-regions (220), to each of which is assigned a congestion contributor. Then, first and second spatial-temporal visual features are determined, and metrics are computed (225), (245), to characterise a degree of dynamic or static congestion in each sub-region. The metrics and congestion contributors are used to generate (260) an indication of the overall measure of congestion within the region of interest.
142 Citations
24 Claims
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1. A method of determining crowd congestion in a physical space by automated processing of a video sequence of the space, the method comprising:
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determining a region of interest in the space; partitioning the region of interest into an irregular array of sub-regions, each comprising a plurality of pixels of video image data; assigning a congestion weighting to each sub-region in the irregular array of sub-regions; determining first spatial-temporal visual features within the region of interest and, for each 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 each sub-region that is not indicated as being dynamically congested, 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 region of interest on the basis of the metrics for the dynamically and statically congested sub-regions and their respective congestion weightings. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24)
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23. A crowd analysis system comprising:
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an imaging device for generating images of a physical space; and
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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 irregular array of sub-regions, each comprising a plurality of pixels of video image data;assign a congestion weighting to each sub-region in the irregular array of sub-regions; determine first spatial-temporal visual features within the region of interest and, for each sub-region, compute a metric based on the said features indicating whether or not the sub-region is dynamically congested; determine second spatial-temporal visual features within the region of interest and, for each sub-region that is not indicated as being dynamically congested, compute a metric based on the said features indicating whether or not the sub-region is statically congested; generate an indication of an overall measure of congestion for the region of interest on the basis of the metrics for the dynamically and statically congested sub-regions and their respective congestion weightings.
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Specification