Modeling, Detection and Recognition of Spatio-Temporal Events from Vide

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

This project addresses the design and analysis of algorithms for the identification of dynamical models of image sequences for the purpose of detection, classification and recognition of spatio-temporal events from video. In particular, we concentrate on (segments of) image sequences that satisfy certain statistical regularity conditions, such as second-order stationarity, or certain physical constraints, such as Lambertian reflection. While this does not cover the most general video sequences, generality will follow from compositionality, by segmenting each sequence into portions that do satisfy the assumptions. The purpose of our models is to enable the detection, classification and recognition of dynamic events, such as the presence of smoke, moving foliage, fire, walking humans etc. in live video.