Mining temporal patterns of movement for video content classification
Michael Fleischman, Phillip Decamp, Deb Kumar Roy · 2006
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.