IDENTIFYING ANOMALOUS OBJECT TYPES DURING CLASSIFICATION
First Claim
1. A computer-implemented method for characterizing one or more objects depicted in frames of video captured by a video camera, the method comprising:
- for each successive video frame;
identifying one or more foreground objects depicted in the video frame, and for each foreground object;
determining one or more appearance characteristics of the foreground object from pixels of the video frame depicting the foreground object, anddetermining one or more kinematic characteristics of the foreground object from the pixels of the video frame depicting the foreground object; and
clustering the determined appearance characteristics and the determined kinematic characteristics of each of the foreground objects to determine one or more object type clusters for classifying objects depicted in the frames of video.
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Abstract
Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
14 Citations
21 Claims
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1. A computer-implemented method for characterizing one or more objects depicted in frames of video captured by a video camera, the method comprising:
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for each successive video frame; identifying one or more foreground objects depicted in the video frame, and for each foreground object; determining one or more appearance characteristics of the foreground object from pixels of the video frame depicting the foreground object, and determining one or more kinematic characteristics of the foreground object from the pixels of the video frame depicting the foreground object; and clustering the determined appearance characteristics and the determined kinematic characteristics of each of the foreground objects to determine one or more object type clusters for classifying objects depicted in the frames of video. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-readable storage medium containing a program which, when executed by a processor, performs an operation for characterizing one or more objects depicted in frames of video captured by a video camera, the operation comprising:
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for each successive video frame; identifying one or more foreground objects depicted in the video frame, and for each foreground object; determining one or more appearance characteristics of the foreground object from pixels of the video frame depicting the foreground object, and determining one or more kinematic characteristics of the foreground object from the pixels of the video frame depicting the foreground object; and clustering the determined appearance characteristics and the determined kinematic characteristics of each of the foreground objects to determine one or more object type clusters for classifying objects depicted in the frames of video. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A system, comprising:
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a video input source configured to provide image data; a processor; and a memory containing a program, which, when executed on the processor is configured to perform an operation for characterizing one or more objects depicted in frames of video captured by a video camera, the operation comprising; for each successive video frame; identifying one or more foreground objects depicted in the video frame, and for each foreground object; determining one or more appearance characteristics of the foreground object from pixels of the video frame depicting the foreground object, and determining one or more kinematic characteristics of the foreground object from the pixels of the video frame depicting the foreground object; and clustering the determined appearance characteristics and the determined kinematic characteristics of each of the foreground objects to determine one or more object type clusters for classifying objects depicted in the frames of video. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification