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. In this learning framework, we evaluate two different approaches to finding an optimal set of tracks under a quadratic model objective, one based on an linear program (LP) relaxation and the other based on novel greedy variants of dynamic programming that handle pairwise interactions. We find the greedy algorithms achieve almost equivalent accuracy to the LP relaxation while being up to 10\(\times \) faster than a commercial LP solver. We evaluate trained models on three challenging benchmarks. Surprisingly, we find that with proper parameter learning, our simple data association model without explicit appearance/motion reasoning is able to achieve comparable or better accuracy than many state-of-the-art methods that use far more complex motion features or appearance affinity metric learning.
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In a recent update of the benchmark server, the organizers changed their evaluation script to count detections in “don’t care” regions as false positives, which we believe is not consistent with general consensus of what “don’t care” regions mean. Thus we report the results up to 24 May 2016 which were evaluated using old evaluation script.
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This work was supported by the US National Science Foundation through Awards IIS-1253538 and DBI-1053036.
Communicated by Xianghua Xie, Mark Jones and Gary Tam.
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Wang, S., Fowlkes, C.C. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. Int J Comput Vis 122, 484–501 (2017). https://doi.org/10.1007/s11263-016-0960-z
- Multi-target tracking
- Data association
- Structured prediction