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Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks - VSSN '06
We present an unsupervised framework to discover characterizations of everyday human activities, and demonstrate how such representations can be used to extract points of interest in event-streams. We begin with the usage of Suffix Trees as an efficient activity-representation to analyze the global structural information of activities, using their local event statistics over the entire continuum of their temporal resolution. Exploiting this representation, we discover characterizingdoi:10.1145/1178782.1178794 fatcat:lynftlpb3zbifacww4a3njwbkm