Robust multiple-vehicle tracking via adaptive integration of multiple visual features

This article presents a robust approach to tracking multiple vehicles with integration of multiple visual features. The observation is modeled by democratic integration strategies according to the reliability of the information in the current multi-visual features to adjust their weights. The appearance model is also embedded in a particle filter (PF) tracking framework. Furthermore, we propose a new model updating algorithm based on the PF. In order to avoid incorrect results caused by "model drift" introduced into the observation model, model updating should only be controlled in a reliable manner, and the rate of updating is based on reliability. This article also presents the experiments using a real video sequence to verify the proposed method.