Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

Jun Xiang; Guoshuai Zhang; Jianhua Hou; Nong Sang; Rui Huang

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arXiv · 1802.03252

Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

Jun Xiang·Guoshuai Zhang·Jianhua Hou·Nong Sang·Rui Huang

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Abstract

Designing a robust affinity model is the key issue in multiple target tracking (MTT). This paper proposes a novel affinity model by learning feature representation and distance metric jointly in a unified deep architecture. Specifically, we design a CNN network to obtain appearance cue tailored towards person Re-ID, and an LSTM network for motion cue to predict target position, respectively. Both cues are combined with a triplet loss function, which performs end-to-end learning of the fused features in a desired embedding space. Experiments in the challenging MOT benchmark demonstrate, that even by a simple Linear Assignment strategy fed with affinity scores of our method, very competitive results are achieved when compared with the most recent state-of-theart approaches.

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BibTeX RIS

Jun Xiang, Guoshuai Zhang, Jianhua Hou, Nong Sang, Rui Huang. 2018-02-09. Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly. https://arxiv.org/abs/1802.03252

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