# Dense Scene Multiple Object Tracking with Box-Plane Matching

arXiv: https://arxiv.org/abs/2007.15576 · HTML: https://mlsanity.com/paper/2007.15576/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [SiamMOT: Siamese Multi-Object Tracking](https://mlsanity.com/paper/2105.11595/) (2021)
- [Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker](https://mlsanity.com/paper/2205.00968/) (2022)
- [FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow](https://mlsanity.com/paper/2308.07207/) (2023)
- [SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking](https://mlsanity.com/paper/2105.11237/) (2021)
- [Enhancing Multiple Object Tracking Accuracy via Quantum Annealing](https://mlsanity.com/paper/2403.18908/) (2024)
- [MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking](https://mlsanity.com/paper/2407.10485/) (2024)
- [EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking](https://mlsanity.com/paper/2210.05278/) (2022)

## Abstract (arXiv)

Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the Layer-wise Aggregation Discriminative Model (LADM) to filter the noisy detections. Then, to associate remaining detections correctly, we introduce the Global Attention Feature Model (GAFM) to extract appearance feature and use it to calculate the appearance similarity between history tracklets and current detections. Finally, we propose the Box-Plane Matching strategy to achieve data association according to the motion similarity and appearance similarity between tracklets and detections. With the effectiveness of the three modules, our team achieves the 1st place on the Track-1 leaderboard in the ACM MM Grand Challenge HiEve 2020.