Multiple feature temporal models for object detection in video

We present a general object detection system for video sequences based on the characterization of the temporal behavior of multiple image features, as well as any dependency relationships that may exist between them. This characterization is based on the coupling of multiple Markov chains and its generalization to coupled Markov random fields. The model automatically learns the temporal behavior of the features that better characterize an object from a training sequence. We show experiments on generic news videos with cluttered backgrounds and we propose a multi-scale process with automatic selection of the optimal scale of the object in the scene.