Papers › Fusion of Head and Full-Body Detectors for Multi-Object Tracking

Fusion of Head and Full-Body Detectors for Multi-Object Tracking

23 May 2017arXiv:1705.08314archive 2025-07-28

Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn

In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach. Yet, relying solely on a single detector is also a major limitation, as useful image information might be ignored. Consequently, this work demonstrates how to fuse two detectors into a tracking system. To obtain the trajectories, we propose to formulate tracking as a weighted graph labeling problem, resulting in a binary quadratic program. As such problems are NP-hard, the solution can only be approximated. Based on the Frank-Wolfe algorithm, we present a new solver that is crucial to handle such difficult problems. Evaluation on pedestrian tracking is provided for multiple scenarios, showing superior results over single detector tracking and standard QP-solvers. Finally, our tracker ranks 2nd on the MOT16 benchmark and 1st on the new MOT17 benchmark, outperforming over 90 trackers.

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