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Multiple object tracking (MOT) is widely applied in video analysis scenarios, such as human interaction,virtual reality, autonomous driving, visual surveillance and robot navigation etc. MOT can be formulated as a sort of tracklets association in existing detection results, in which the accuracy of detection algorithm is entitled an essential role in tracking performance. We proposed a multiple object tracking algorithm via coUaborative motion status estimation. The algorithm is based on the tracking-by-detection framework. The algorithm predominantly focuses on data association of adjacent video frames?tackling challenges of MOT from three aspects:object detection,object motion status estimation and data association. Firstly,as for object detection, multi scale convolutional neural network(MS ĈNN) is adopted as the detector, since the advantage of deep learning in detection outweighs that of classical machine learning method. Se­ condly, to better predict object motion status and handle occlusion among targets? different motion estimation methods are utilized according to different motion statuses. In tracking status, kernelized correlation filter is employed, while in occlusion status,the use of kalman filter is prioritized. Lastly,Kuhn-Munkres algorithm is adopted to work out data as­ sociation between detections and tracklets. A substantial amount of experiments were carried out to estimate the effi­ ciency. The results are quite positive,demonstrating high accuracy.

协同运动状态估计的多目标跟踪算法 (Multiple Object Tracking Algorithm via Collaborative Motion Status Estimation).