# Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation

arXiv: https://arxiv.org/abs/1511.06654 · HTML: https://mlsanity.com/paper/1511.06654/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2015.

## Built on by (most cited first, 5 of 5)

- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking](https://mlsanity.com/paper/2103.16178/) (2021)
- [Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting](https://mlsanity.com/paper/2312.05923/) (2023)
- [Learnable Graph Matching: A Practical Paradigm for Data Association](https://mlsanity.com/paper/2303.15414/) (2023)
- [Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering](https://mlsanity.com/paper/2508.05172/) (2025)

## Abstract (arXiv)

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e. during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.