Abstract: A method of segmenting a sequence of video images according to scene activity, the method comprising: defining a first series of nodes in a first multi-dimensional space, each node corresponding to an image of the sequence of video images; defining a transformation function that maps each of the first series of nodes to a corresponding node in a second multi-dimensional space having a lower dimensionality than the first multi-dimensional space; applying said transformation function to each of the first series of nodes to define a second series of respective nodes in the second multi-dimensional space; applying a data clustering algorithm to the second series of nodes to identify clusters of nodes within the second multi-dimensional space, the data clustering algorithm being constrained by a measure of feature distance between a pair of clusters of nodes and a measure of temporal distance between the pair of clusters of nodes; determining a representative image from each cluster of nodes and plotting each repr

Abstract: A method of segmenting a sequence of video images according to scene activity, the method comprising: defining a first series of nodes in a first multi-dimensional space, each node corresponding to an image of the sequence of video images; defining a transformation function that maps each of the first series of nodes to a corresponding node in a second multi-dimensional space having a lower dimensionality than the first multi-dimensional space; applying said transformation function to each of the first series of nodes to define a second series of respective nodes in the second multi-dimensional space; applying a data clustering algorithm to the second series of nodes to identify clusters of nodes within the second multi-dimensional space, the data clustering algorithm being constrained by a measure of feature distance between a pair of clusters of nodes and a measure of temporal distance between the pair of clusters of nodes; determining a representative image from each cluster of nodes and plotting each repr

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.

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.