Method for building and extracting entity networks from video
First Claim
1. A computer implemented method for deriving, from video data, an association between at least two entities in motion, comprising:
- extracting the entities from the video data;
tracking trajectories of the entities based on the video data to form two or more tracklets;
deriving one or more associations between the entities by;
detecting an event based on at least one spatio-temporal motion correlation between the entities;
calculating a similarity measure of the closeness of the tracklets;
identifying entity behaviors comprising at least one of spatial actions and behavioral action;
performing pattern analysis to group the tracklets and sites; and
merging an event ontology with hierarchical weighted graph matching to reduce candidate space wherein the candidate space comprises all entities to be tracked.
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Abstract
A computer implemented method for deriving an attribute entity network (AEN) from video data is disclosed, comprising the steps of: extracting at least two entities from the video data; tracking the trajectories of the at least two entities to form at least two tracks; deriving at least one association between at least two entities by detecting at least one event involving the at least two entities, said detecting of at least one event being based on detecting at least one spatio-temporal motion correlation between the at least two entities; and constructing the AEN by creating a graph wherein the at least two objects form at least two nodes and the at least one association forms a link between the at least two nodes.
45 Citations
20 Claims
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1. A computer implemented method for deriving, from video data, an association between at least two entities in motion, comprising:
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extracting the entities from the video data; tracking trajectories of the entities based on the video data to form two or more tracklets; deriving one or more associations between the entities by;
detecting an event based on at least one spatio-temporal motion correlation between the entities;calculating a similarity measure of the closeness of the tracklets; identifying entity behaviors comprising at least one of spatial actions and behavioral action; performing pattern analysis to group the tracklets and sites; and merging an event ontology with hierarchical weighted graph matching to reduce candidate space wherein the candidate space comprises all entities to be tracked. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An apparatus for deriving an association between at least two entities in motion from video data captured by at least one sensor, comprising:
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a processor communicatively connected to said at least one sensor, the processor being configured for; extracting the entities from the video data; tracking trajectories of the entities based on the video data to form two or more tracklets; and deriving one or more associations between the entities by;
detecting an event based on at least one spatio-temporal motion correlation between the entities;calculating a similarity measure of the closeness of the tracklets; identifying entity behaviors comprising at least one of spatial actions and behavioral action; performing pattern analysis to group the tracklets and sites; and merging an event ontology with hierarchical weighted graph matching to reduce candidate space wherein the candidate space comprises all entities to be tracked. - View Dependent Claims (9, 10, 11, 12)
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13. A computer implemented method for deriving, from video data, an association between at least two entities in motion, comprising:
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extracting the entities from the video data; tracking trajectories of the entities; deriving one or more associations between the entities by detecting at least one event based on at least one spatio-temporal motion correlation between the entities; and merging an event ontology with hierarchical weighted graph matching to reduce candidate space wherein the candidate space comprises all entities to be tracked. - View Dependent Claims (14, 15, 16)
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17. A computer implemented method for deriving, from video data, an association between at least two entities in motion, comprising:
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extracting the entities from the video data; tracking trajectories of the entities; deriving one or more associations between the entities by detecting at least one event based on at least one spatio-temporal motion correlation between the entities; and employing a Markov Logic Network for reasoning and inferencing in visual and geo-spatial domains. - View Dependent Claims (18, 19, 20)
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Specification