Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit proxy for the target tracking metric. Whether using only simple geometric features or more sophisticated ones that also take appearance into account, our approach outperforms the state-of-the-art on several MOT benchmarks.

Presented at:
Computer Vision and Patter Recognition (CVPR), Long Beach, CA, June, 2019
Jun 23 2019

 Record created 2019-05-23, last modified 2019-12-05

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