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An Online and Flexible Multi-object Tracking Framework Using Long Short-Term Memory
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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
doi:10.1109/cvprw.2018.00169
dblp:conf/cvpr/WanWZ18
fatcat:pywro5p5rner7hvlpojtoaweiy