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Video surveillance is an alternative approach to staff or selfreporting that has the potential to detect and monitor aggressive behaviors more accurately. In this paper, we propose an automatic algorithm capable of recognizing aggressive behaviors from video records using local binary motion descriptors. The proposed algorithm will increase the accuracy for retrieving aggressive behaviors from video records, and thereby facilitate scientific inquiry into this low frequency but high impact phenomenon that eludes other measurement approaches.doi:10.1145/1461893.1461905 dblp:conf/mm/ChenCWGB08 fatcat:2oyp7hyjzzafzcf6k5cuf5er2q