A dense pedestrian tracking method based on fusion features under multi-vision

Many multi-object pedestrian tracking algorithms have been proposed in computer vision, and great progress has been made in tracking efficiency and accuracy recently. Practical applications are severely hampered by the fact that the majority of tracking techniques now in use are still unable to address the issues of object occlusion and reappearance in camera perspectives. To tackle the above problems in dense crowds under multi-vision, the multi-target pedestrian tracking method is based on fusion feature correlation. The feature pool was updated based on GMM to reduce feature pollution caused by dense people. To ensure the tracking universality, the similarity threshold of target features was calculated dynamically based on K-means. The similarity of fused features is used to associate the pedestrian features, with the homography constraint check to determine the addition and reappearance of pedestrians, which reduces error and miss tracking. The results of experiments using several algorithms on the public dataset Shelf indicate that the suggested method's average accuracy is 16.05% and 7.39% higher than that of other methods, while its average success rate is 16.04% and 4.16% higher. The average error tracking rate under the complete video is 10.11%, which achieves significant results in controlling mistracking and effectively associates with the original ID after the pedestrian’s reappearance.