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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 noveldoi:10.1109/cvprw.2018.00169 dblp:conf/cvpr/WanWZ18 fatcat:pywro5p5rner7hvlpojtoaweiy