| 摘要 |
Embodiments of the present invention describe a collaborative framework for mining of surveillance videos to detect abnormal events, which introduces a two-stage training process to alleviate the high false alarm problem. In the first stage, unsupervised clustering is performed on the segments of the video streams and a set of abnormal events are combined with user feedback to generate a clean training set. In the second stage, the clean training set is used to train a more precise model for the analysis of normal events and the motion detection results from multiple cameras can be cross validated and combined. This description is not intended to be a complete description of, or limit the scope of, the invention. Other features, aspects, and objects of the invention can be obtained from a review of the specification, the figures, and the claims.
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