Classification and retrieval of traffic video using auto-regressive stochastic processes

Abstract

We propose to model the traffic flow in a video using a holistic generative model that does not require segmentation or tracking. In particular, we adopt the dynamic texture model, an auto-regressive stochastic process, which encodes the appearance and the underlying motion separately into two probability distributions. With this representation, retrieval of similar video sequences and classification of traffic congestion can be performed using the Kullback-Leibler divergence and the Martin distance. Experimental results show good retrieval and classification performance, with robustness to environmental conditions such as variable lighting and shadows.