Vision-based multiple vehicle detection and tracking at nighttime

In this paper, we develop a robust vision-based approach for real-time traffic data collection at nighttime. The proposed algorithm detects and tracks vehicles through detection and location of vehicle headlights. First, we extract headlights candidates by an adaptive image segmentation algorithm. Then we group headlights candidates that belong to the same vehicle by spatial clustering and generate vehicle hypotheses by rule-based reasoning. The potential vehicles are then tracked over frames by region search and pattern analysis methods. The spatial and temporal continuity extracted from tracking process is used to confirm vehicle's presence. To handle problem of occlusions, we apply Kalman Filter to motion estimation. We test the algorithm on the video clips of nighttime traffic under different conditions. The experimental results show that real-time vehicle counting and tacking for multi-lanes are achieved and the total detection rate is above 96%.