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Joint Headlight Pairing and Vehicle Tracking by Weighted Set Packing in Nighttime Traffic Videos

We propose a set packing (SP) framework for joint headlight pairing and vehicle tracking. Given headlight detections, traditional nighttime vehicle tracking methods usually first pair headlights and then track these pairs. However, the poor photometric condition often introduces tremendous noises in headlight detection and pairing, which leads to unrecoverable errors for vehicle tracking. To overcome the challenge, we propose to jointly model these two tasks in a weighted SP framework. Specifically, a graph is built which takes candidate pair track hypotheses as nodes and encodes in edges both the disjoint constraints for tracking and the no-sharing-headlight constraints for pairing. Solving a weighted SP problem on such a graph produces vehicle trajectories, and facilitates pairing with temporal context and in turn produces high quality vehicle trajectories. The solution, however, raises the issue of unmanageable graph scale since the number of track hypotheses grows exponentially over time. To address this issue, pruning strategies are developed to solve the joint model efficiently. The proposed system is evaluated on two traffic data sets, including videos under various challenging conditions. Both quantitative and qualitative results show that our method outperforms other tested methods, both in nighttime vehicle tracking and in multi-target tracking, confirming the benefits of jointly modeling the two tasks.