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• W2948777210 abstract "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." @default.
• W2948777210 created "2019-06-14" @default.
• W2948777210 creator A5038674741 @default.
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• W2948777210 date "2019-06-01" @default.
• W2948777210 modified "2023-10-02" @default.
• W2948777210 title "Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking" @default.
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• W2948777210 doi "https://doi.org/10.1109/cvpr.2019.00477" @default.
• W2948777210 hasPublicationYear "2019" @default.
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