Unsupervised analysis of activity sequences using event-motifs

Raffay Hamid, Siddhartha Maddi, Aaron Bobick, Irfan Essa
2006 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 characterizing
more » ... uences and present their usage in an ensemble-based framework for activity classification. Finally, we propose a method to automatically detect subsequences of events that are locally atypical in a structural sense. Results over extensive data-sets, collected from multiple sensor-rich environments are presented, to show the competence and scalability of the proposed framework.
doi:10.1145/1178782.1178794 fatcat:lynftlpb3zbifacww4a3njwbkm