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On-line abnormality detection in video without the use of object detection and tracking is a desirable task in surveillance.We address this problem for the case when labeled information about normal events is limited and information about abnormal events is not available. We formulate this problem as a one-class classification, where multiple local novelty classifiers (detectors) are used to first...
A scanning window type pedestrian detector is presented that uses both appearance and motion information to find walking people in surveillance video. We extend the work of Viola, Jones and Snow (2005) to use many more frames as input to the detector thus allowing a much more detailed analysis of motion. The resulting detector is about an order of magnitude more accurate than the detector of Viola,...
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