Learning Background and Shadow Appearance with 3-D Vehicle Models ∗

Abstract

This paper presents a novel algorithm for simultaneous background appearance modeling and coarse-scale vehicle recognition in traffic surveillance applications. 3-d mesh models representing a small set of vehicle classes are used to the hypothesize image segmentations into background, shadow, and vehicle regions. The algorithm optimizes vehicle class and motion parameters to best agree with a Hidden Markov Model for the image appearance. The best hypothesis, combined with image data, is used to adapt the parameters of the appearance model. Experiments on real video show that an appearance model trained in this way performs almost as well as one trained using manually segmented images.