A Close Multi-Target Tracking Algorithm Based on Weight Correction

Abstract

When multiple targets are close to each other and intersect, the Gaussian mixture probability hypothesis density (GM-PHD) filtering algorithm experiences degraded tracking performance. To address this problem, a neighborhood multi-target tracking optimization algorithm based on weight correction is proposed. In the proposed method, a proximity monitoring mechanism is first introduced to detect the distance between targets. Next, the similarity between the measured data and the target predicted value is calculate to form a similarity matrix. If there are multiple data points in a row of the similarity matrix exceed the threshold, further correction should be performed on the data in that row. Finally, the weight correction matrix is formed by combining the above two steps. Simulation results demonstrate that the tracking accuracy and stability of the proposed algorithm are significantly improved in scenarios of multi-target intersection and parallel tracking, and its performance is better than that of the traditional GM-PHD filtering algorithm.