An Online and Flexible Multi-Object Tracking Framework Using Long Short-Term Memory

Xingyu Wan, Jinjun Wang, Sanping Zhou

Abstract: The capacity to model temporal dependency by Recurrent Neural Networks (RNNs) makes it a plausible selection for the multi-object tracking (MOT) problem. Due to the non-linear transformations and the unique memory mechanism, Long Short-Term Memory (LSTM) can consider a window of history when learning discriminative features, which suggests that the LSTM is suitable for state estimation of target objects as they move around. This paper focuses on association based MOT, and we propose a novel Siamese LSTM Network to interpret both temporal and spatial components nonlinearly by learning the feature of trajectories, and outputs the similarity score of two trajectories for data association. In addition, we also introduce an online metric learning scheme to update the state estimation of each trajectory dynamically. Experimental evaluation on MOT16 benchmark shows that the proposed method achieves competitive performance compared with other state-of-the-art works.