Detection confidence driven multi-object tracking to recover reliable tracks from unreliable detections
Travis Mandel, Mark Jimenez, Emily Risley, Taishi Nammoto, Rebekka Williams, Max Panoff, Meynard Ballesteros, Bobbie Suarez
Abstract
Multi-object tracking (MOT) systems often rely on accurate object detectors; however, accurate detectors are not available in every application domain. We present Robust Confidence Tracking (RCT), an offline MOT algorithm designed for settings where detection quality is poor. Whereas prior methods simply threshold and discard detection confidence information, RCT relies on the exact detection confidence values to increase track quality throughout the entire tracking pipeline. This innovation (along with some simple and well-studied heuristics) allows RCT to achieve robust performance with minimal identity switches, even when provided with completely unfiltered detections. To compare trackers in the presence of unreliable detections, we present a challenging real-world underwater fish tracking dataset, FISHTRAC. In an large-scale evaluation across FISHTRAC, UA-DETRAC, and MOTChallenge data, RCT outperforms a wide variety of trackers, including deep trackers and more classic approaches. We have publically released our FISHTRAC codebase and training dataset at https://github.com/tmandel/fish-detrac, which will facilitate comparing trackers on understudied problems.