Weighted symbolic analysis of human behavior for event detection

Automatic video analysis and understanding has become a high interest research topic, with applications to video browsing, content-based video indexing, and visual surveillance. However, the automation of this process is still a challenging task, due to clutters produced by low-level processing operations. This common problem can be solved by embedding significant contextual information into the data, as well as using simple syntactic approaches to perform the matching between actual sequences and models. In this context we propose a novel framework that employs a symbolic representation of complex activities through sequences of atomic actions based on a weighted Context-Free Grammar. © 2013 SPIE-IS&T.