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UNSUPERVISED LEARNING OF FEATURE ANOMALIES FOR A VIDEO SURVEILLANCE SYSTEM
UNSUPERVISED LEARNING OF FEATURE ANOMALIES FOR A VIDEO SURVEILLANCE SYSTEM
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机译:视频监控系统功能异常的无知学习
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摘要
Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. In one embodiment, e.g., a machine learning engine may include statistical engines for generating topological feature maps based on observations and a detection module for detecting feature anomalies. The statistical engines may include adaptive resonance theory (ART) networks which cluster observed position-feature characteristics. The statistical engines may further reinforce, decay, merge, and remove clusters. The detection module may calculate a rareness value relative to recurring observations and data in the ART networks. Further, the sensitivity of detection may be adjusted according to the relative importance of recently observed anomalies.
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