Learning Optimal Parameters For Multi-target Tracking

Shaofei Wang and Charless Fowlkes

Abstract

We describe an end-to-end framework for learning parameters of min-cost flow multi-target tracking problem with quadratic trajectory interactions including suppression of overlapping tracks and contextual cues about co-occurrence of different objects. Our approach utilizes structured prediction with a tracking-specific loss function to learn the complete set of model parameters. Under our learning framework, we evaluate two different approaches to finding an optimal set of tracks under quadratic model objective based on an LP relaxation and a novel greedy extension to dynamic programming that handles pairwise interactions. We find the greedy algorithm achieves almost equivalent accuracy to the LP relaxation while being 2-7x faster than a commercial solver. We evaluate trained models on the challenging MOT and KITTI benchmarks. Surprisingly, we find that with proper parameter learning, our simple data-association model without explicit appearance/motion reasoning is able to outperform many state-of-the-art methods that use far more complex motion features and affinity metric learning.

Session

Tracking

Files

Extended Abstract (PDF, 1570K)
Paper (PDF, 3M)
Supplemental Materials (ZIP, 7M)

DOI

10.5244/C.29.4
https://dx.doi.org/10.5244/C.29.4

Citation

Bibtex

@inproceedings{BMVC2015_4,
	title={Learning Optimal Parameters For Multi-target Tracking},
	author={Shaofei Wang and Charless Fowlkes},
	year={2015},
	month={September},
	pages={4.1-4.13},
	articleno={4},
	numpages={13},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.4},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.4}
}