Location-aware event detection
First Claim
Patent Images
1. A method for detecting one or more events, comprising:
- using multiple overlapping regions of interest on a video sequence to cover a respective location for one or more events at a point of sale, wherein each event is associated with at least one of the multiple overlapping regions of interest;
applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models, wherein applying multiple-instance learning comprises using a learning technique to learn the one or more location-aware event models for events including at least a pickup and a drop; and
applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.
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Abstract
Techniques for detecting one or more events are provided. The techniques include using multiple overlapping regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the multiple overlapping regions of interest, applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models, and applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.
45 Citations
17 Claims
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1. A method for detecting one or more events, comprising:
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using multiple overlapping regions of interest on a video sequence to cover a respective location for one or more events at a point of sale, wherein each event is associated with at least one of the multiple overlapping regions of interest; applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models, wherein applying multiple-instance learning comprises using a learning technique to learn the one or more location-aware event models for events including at least a pickup and a drop; and applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer program product comprising a tangible computer readable recordable storage medium having computer readable program code for detecting one or more events, said computer program product including:
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computer readable program code for using multiple overlapping regions of interest on a video sequence to cover a respective location for one or more events at a point of sale, wherein each event is associated with at least one of the multiple overlapping regions of interest; computer readable program code for applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models, wherein applying multiple-instance learning comprises using a learning technique to learn the one or more location-aware event models for events including at least a pickup and a drop; and computer readable program code for applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A system for detecting one or more events, comprising:
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a memory; and at least one processor coupled to said memory and operative to; use multiple overlapping regions of interest on a video sequence to cover a respective location for one or more events at a point of sale, wherein each event is associated with at least one of the multiple overlapping regions of interest; apply multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models, wherein applying multiple-instance learning comprises using a learning technique to learn the one or more location-aware event models for events including at least a pickup and a drop; and apply the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events. - View Dependent Claims (15, 16, 17)
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Specification