Multi-target tracking method based on multi-model fusion and data association

The invention discloses a multi-target tracking method based on multi-model fusion and data association; the tracking method comprises the following steps: firstly using an interframe difference method to detect a motion target contour and center of mass coordinates; fusing a pyramid optical flow method with Kalman filtering so as to predict the center of mass coordinates of the motion target in the next moment; using Euclidean distances between the center of mass coordinate predicted value and the center of mass coordinate detection value at next moment to form a benefit matrix, and using a Hungary algorithm to obtain the optimal matching through data association; finally removing certain portion unable to satisfy requirements in a tracker, and building a tracking unit for non-assigned detections, thus realizing multi-target tracking. The tracking method can be less affected by light changes and background noise interferences, thus solving the tracking failures caused by target blocking or mutual interferences between targets, providing multi-target tracking accuracy, and providing well instantaneity and robustness.