CLUSTERING-BASED OBJECT CLASSIFICATION
First Claim
1. A method for identifying objects in video content comprising:
- receiving video content of a scene captured by a video camera;
detecting an object in the video content;
identifying a track that the object follows over a series of frames of the video content;
extracting object features for the object from the video content; and
classifying the object based on the object features, wherein classifying the object further comprises;
determining a track-level classification for the object using spatially invariant object features;
determining a global-clustering classification for the object using spatially variant features; and
determining an object type for the object based on the track-level classification and the global-clustering classification for the object.
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Accused Products
Abstract
An example of a method for identifying objects in video content according to the disclosure includes receiving video content of a scene captured by a video camera, detecting an object in the video content, identifying a track that the object follows over a series of frames of the video content, extracting object features for the object from the video content, and classifying the object based on the object features. Classifying the object further comprises: determining a track-level classification for the object using spatially invariant object features, determining a global-clustering classification for the object using spatially variant features, and determining an object type for the object based on the track-level classification and the global-clustering classification for the object.
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Citations
28 Claims
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1. A method for identifying objects in video content comprising:
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receiving video content of a scene captured by a video camera; detecting an object in the video content; identifying a track that the object follows over a series of frames of the video content; extracting object features for the object from the video content; and classifying the object based on the object features, wherein classifying the object further comprises; determining a track-level classification for the object using spatially invariant object features; determining a global-clustering classification for the object using spatially variant features; and determining an object type for the object based on the track-level classification and the global-clustering classification for the object. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A surveillance system configured to identify objects in video content captured by a video camera, the system comprising:
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means for receiving video content of a scene captured by a video camera; means for detecting an object in the video content; means for identifying a track that the object follows over a series of frames of the video content; means for extracting object features for the object from the video content; and means for classifying the object based on the object features, wherein classifying the object further comprises; means for determining a track-level classification for the object using spatially invariant object features; means for determining a global-clustering classification for the object using spatially variant features; and means for determining an object type for the object based on the track-level classification and the global-clustering classification for the object. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A surveillance system for identifying objects in video content captured by a video camera, the system comprising:
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a tangible, non-transitory computer-readable memory; a plurality of modules comprising processor executable code stored in the memory; a processor connected to the memory and configured to access the plurality of modules stored in the memory; and a video processing module configured to; receive video content of a scene captured by a video camera; detect an object in the video content; identify a track that the object follows over a series of frames of the video content; extract object features for the object from the video content; and classify the object based on the object features, wherein to classify the object the video processing module is further configured to; determine a track-level classification for the object using spatially invariant object features; determine a global-clustering classification for the object using spatially variant features; and determine an object type for the object based on the track-level classification and the global-clustering classification for the object. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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22. A tangible computer-readable medium, having stored thereon computer-readable instructions identifying objects in video content, comprising instructions configured to cause a computer to:
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receive video content of a scene captured by a video camera; detect an object in the video content; identify a track that the object follows over a series of frames of the video content; extract object features for the object from the video content; and classify the object based on the object features, wherein the instruction to cause the computer to classify the object further comprise instructions to cause the computer to; determine a track-level classification for the object using spatially invariant object features; determine a global-clustering classification for the object using spatially variant features; and determine an object type for the object based on the track-level classification and the global-clustering classification for the object. - View Dependent Claims (23, 24, 25, 26, 27, 28)
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Specification