Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking

• URL: http://arxiv.org/abs/2011.07101v1
• Date: Fri, 13 Nov 2020 19:36:56 GMT
• Title: Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking
• Abstract summary: We propose the Joint Posterior Tracker (JPT) to robustly reasons over the posterior of associations and trajectories. JPT exhibits more accurate uncertainty representation of data associations with superior performance on standard metrics. We also show the utility of JPT applied to automatic scheduling of user-in-the-loop annotations for improved trajectory quality.
• Score: 12.993112198633016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust data association is critical for analysis of long-term motion trajectories in complex scenes. In its absence, trajectory precision suffers due to periods of kinematic ambiguity degrading the quality of follow-on analysis. Common optimization-based approaches often neglect uncertainty quantification arising from these events. Consequently, we propose the Joint Posterior Tracker (JPT), a Bayesian multi-object tracking algorithm that robustly reasons over the posterior of associations and trajectories. Novel, permutation-based proposals are crafted for exploration of posterior modes that correspond to plausible association hypotheses. JPT exhibits more accurate uncertainty representation of data associations with superior performance on standard metrics when compared to existing baselines. We also show the utility of JPT applied to automatic scheduling of user-in-the-loop annotations for improved trajectory quality.

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