Multiple-Target Tracking for Crossroad Traffic Utilizing Modified Probabilistic Data Association
Hsu-Yung Cheng, Jenq Neng Hwang · 2007
A multiple-target tracking system aimed at analyzing crossroad traffic systematically is proposed in this paper. The proposed mechanism is based on Kalman filtering and modified probabilistic data association. Unlike traditional Kalman filtering tracking, the proposed mechanism constructs candidate measurement lists by matching the sizes of the measurements and the targets first. When the sizes do not match, object matching within a limited area is performed. Also, we modify the classical probabilistic data association method to enhance its performance and make it more suitable for vision-based systems. The proposed mechanism, which can serve as the foundation for automatic traffic event detection, can solve the occlusion problems effectively without incurring too much computational complexity.