Improved Joint Probabilistic Data Association Multi-target Tracking Algorithm Based on Camera-Radar Fusion

A Joint Probabilistic Data Association (JPDA) multi-objective tracking improvement algorithm based on camera-radar fusion is proposed to address the problems of poor single-sensor tracking performance, unknown target detection probability, and missing valid targets in complex traffic scenarios. First, according to the correlation rule between the target track and the measurement, the correlation probability between the target and the measurement is obtained; then the measurement collection is divided into camera-radar measurement matched target, camera-only measurement matched target, radar-only measurement matched target, and no-match target; and the correlation probability is corrected with different confidence levels to avoid the use of unknown detection probability. The multi-target tracking algorithm, the multi-sensor correlation algorithm based on the correlation sequential correlation method, and the scalar-weighted Kalman fusion algorithm achieve stable tracking and accurate fusion of targets. Finally, the experimental vehicle equipped with millimeter-wave radar and camera was tested under real traffic conditions, and the test results show that the target is stably tracked and the fusion result has good accuracy, which solves the problem of effective target loss and verifies the feasibility and effectiveness of the algorithm.