| Summary: | 碩士 === 南華大學 === 資訊管理學系碩士班 === 98 === As the progress of technology, the surveillance system is installed more and more widely. It usually requires a lot of time and human efforts to check if a specified event occurs in the captured video. To solve this problem, there are many approaches were proposed to recognize behaviors of a video object automatically. Among available behavior recognition methods, the MHI-based approaches are more popular for they have less computational complexity and are easier than another. In this thesis, a fast MHI approach is proposed to reduce the computation time of the MHI approach by storing the multiple sets of features for a predefined behavior, using the partial distance computation method, and changing the calculated order. Nine local information proposed by Chen and squared Euclidean distance are used in the behavior matching process in this thesis to manifest the performance of the proposed approach. Experiment results show that the proposed method can effectively reduce the computation time of MHI approach.
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