Vehicle detection and tracking under various lighting conditions using a particle filter

The authors propose a vision-based automatic system to detect preceding vehicles on the highway under various lighting and different weather conditions. To adapt to different characteristics of vehicle appearance under various lighting conditions, four cues including underneath shadow, vertical edge, symmetry and taillight are fused for the vehicle detection. The authors achieve this goal by generating probability distribution of vehicle under particle filter framework through the processes of initial sampling, propagation, observation, cue fusion and evaluation. Unlike normal particle filter focusing on single target distribution in a state space, the authors detect multiple vehicles with a single particle filter through a high-level tracking strategy using clustering. In addition, the data-driven initial sampling technique helps the system detect new objects and prevent the multi-modal distribution from collapsing to the local maxima. Experiments demonstrate the effectiveness of the proposed system.