# Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

arXiv: https://arxiv.org/abs/1802.03252 · HTML: https://mlsanity.com/paper/1802.03252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Spatio-Temporal Point Process for Multiple Object Tracking](https://mlsanity.com/paper/2302.02444/) (2023)

## Abstract (arXiv)

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.