Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

Understand

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.