Abstract: For short term occlusion and detector errors in online multiple object tracking, a new algorithm based on parameter learning and motion prediction is proposed. Firstly, the Kalman filter model is established by using the historical trajectory of the target, and target possible position in the current frame is given. Then, the cost matrix is established by calculating the correlation between the target and the current observation. The multi-target tracking is modeled as an assignment problem, and the Hungarain algorithm is used to solve the problem. In addition, the unusual situation of the target entering, disappearing and occlusion are processed. For the parameters of the multi-target tracking system, a new binary classification learning scheme is designed. Experimental results verify the effectiveness of parameter learning and the robustness against false detection, missed detection and occlusion. The proposed method has some advantages in many aspects compared with the performance of several classical algorithms.

Key words: online multiple object tracking, Kalman filter, assignment problem, parameter learning, motion prediction