Multi-candidate association online multi-target tracking based on R-FCN framework

Online multi-target tracking is an important prerequisite for real-time video sequence analysis. Because of low reliability in target detection, high tracking loss rate and unsmooth trajectory in online multi-target tracking, an online multi-target tracking model based on R-FCN (region based fully convolutional networks) network framework is proposed. Firstly, the target evaluation function based on R-FCN network framework is used to select more reliable candidates in the next frame between KF and detection results. Second, the Siamese network is used to perform similarity measurement based on appearance features to complete the match between candidates and tracks. Finally, the tracking trajectory is optimized by the RANSAC (Random sample consensus) algorithm. In crowded and partially occluded complex scenes, the proposed algorithm has higher target recognition ability, greatly reduces the phenomenon of missed detection and false detection, and the tracking track is more continuous and smooth. The experimental results show that under the same conditions, compared with the existing methods, the performance indicators of the proposed method, such as target tracking accuracy (MOTA), number of lost trajectories (ML) and number of false positives (FN), have been greatly improved.