# A Hybrid Data Association Framework for Robust Online Multi-Object Tracking

arXiv: https://arxiv.org/abs/1703.10764 · HTML: https://mlsanity.com/paper/1703.10764/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2017.

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

- [Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration](https://mlsanity.com/paper/2105.14683/) (2021)
- [Spatio-Temporal Point Process for Multiple Object Tracking](https://mlsanity.com/paper/2302.02444/) (2023)
- [Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking](https://mlsanity.com/paper/2112.07664/) (2021)
- [Multi-Object Tracking with Deep Learning Ensemble for Unmanned Aerial System Applications](https://mlsanity.com/paper/2110.02044/) (2021)
- [An Approximate Dynamic Programming Framework for Occlusion-Robust Multi-Object Tracking](https://mlsanity.com/paper/2405.15137/) (2024)

## Abstract (arXiv)

Global optimization algorithms have shown impressive performance in data-association based multi-object tracking, but handling online data remains a difficult hurdle to overcome. In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking. We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames. More specifically, in the min-cost multi-commodity network flow, the target-specific similarities are online learned to enforce the local consistency for reducing the complexity of the global data association. Meanwhile, the global data association taking multiple video frames into account alleviates irrecoverable errors caused by the local data association between adjacent frames. To ensure the efficiency of online tracking, we give an efficient near-optimal solution to the proposed min-cost multi-commodity flow problem, and provide the empirical proof of its sub-optimality. The comprehensive experiments on real data demonstrate the superior tracking performance of our approach in various challenging situations.