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Designing a robust affinity model is the key issue in multiple targettracking (MTT). This paper proposes a novel affinity model by learning featurerepresentation and distance metric jointly in a unified deep architecture.Specifically, we design a CNN network to obtain appearance cue tailored towardsperson Re-ID, and an LSTM network for motion cue to predict target position,respectively. Both cues are combined with a triplet loss function, whichperforms end-to-end learning of the fused features in a desired embeddingspace. Experiments in the challenging MOT benchmark demonstrate, that even by asimple Linear Assignment strategy fed with affinity scores of our method, verycompetitive results are achieved when compared with the most recentstate-of-theart approaches.

Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly