Mining temporal patterns of movement for video content classification

Authors:
Michael Fleischman;Phillip Decamp;Deb Roy
Affiliations:
Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology
Venue:
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Year:
2006

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Abstract

Scalable approaches to video content classification are limited by an inability to automatically generate representations of events that encode abstract temporal structure. This paper presents a method in which temporal information is captured by representing events using a lexicon of hierarchical patterns of movement that are mined from large corpora of unannotated video data. These patterns are then used as features for a discriminative model of event classification that exploits tree kernels in a Support Vector Machine. Evaluations show the method learns informative patterns on a 1450-hour video corpus of natural human activities recorded in the home.