Multi-camera association tracking algorithm for pedestrian target based on difference image

Abstract

The current pedestrian target tracking algorithm (such as adjacent frame matching target tracking algorithm, deep learning YOLOv5 algorithm, etc.) ignores pedestrian foreground image segmentation, resulting in significant errors in pedestrian target tracking and insufficient tracking results. Therefore, a multi-camera association tracking algorithm for pedestrians and targets based on differential images is designed. Multi-camera devices are used to collect pedestrian video sequence images, and the key frame difference image sample set is extracted. The initial background of the pedestrian image is modeled, and the foreground image is differentially segmented to construct the initial model of the differential image. The DeepSORT algorithm is used to complete the multi-pedestrian target association. The pedestrian target obeys the Laplacian random variable probability density function, and moves according to the center position of the bounding box to ensure that the target tends to move around the starting position, and realizes the multi-camera association tracking. The research method achieved maximum MOTA and MOTP values of 18.87 % and 99.22 % under different experimental times, demonstrating good association tracking ability. Moreover, the maximum comprehensive index of multiple pedestrian target association results approached 100 %, while the minimum value far exceeded 95 %. The tracking comprehensiveness and trajectory interruption rate of the research method were 98 % and 1.2 %, respectively, which were significantly better than other comparison algorithms. The processing speed reached 25FPS, effectively balancing computational efficiency. The experimental results verify that the proposed algorithm has ideal application effects.