MF-SORT: Simple Online and Realtime Tracking with Motion Features

Abstract

Multiple object tracking (MOT) plays a key role in video analysis. On MOT, DeepSORT (Simple Online and Realtime Tracking with a deep association metric) performs effectively by combining features of appearance and motion for estimating data association. However, computing with multiple features are time consuming. In certain applications, cameras are static, such as pedestrian surveillance, sports video analysis and so on. Here, without camera movement the motion trajectories of objects are generally possible to estimate. The introduction of more features cannot improve the performance of object tracking discriminatively. Furthermore, the time cost rises evidently. To address this problem, we propose a novel Simple Online and Realtime Tracking with motion features (MF-SORT). By focusing on the motion features of the objects during data association, the proposed scheme is able to take a trade-off between performance and efficiency. The experimental results on the MOT Challenge benchmark and MOT-SOCCER (newly established in this work) demonstrate that the proposed method is much faster than DeepSORT with the comparable accuracy.