UniRTL: A universal RGBT and low-light benchmark for object tracking

Solving single- and multiple-object tracking problems with a single network is challenging in the RGBT tracking. We present a universal RGBT and low-light benchmark (UniRTL), which contains 3 x 626 videos for SOT and 3 x 50 videos for MOT, totally with more than 158K frame triplet. The dataset is divided into low-, middle-, and high-illuminance categories based on the measurement of the scene illuminance. We also propose a SOT and MOT unified tracking-with-detection tracker (Unismot) that comprises a detector, first-frame target prior (FTP), and data associator. SOT and MOT are unified by feeding FTP into the detector and data associator. Re-ID long-term matching module and reusing low-score bounding boxes are proposed to augment SOT and MOT performance, respectively. Experiments demonstrate that Unismot performs as well as or better than its counterparts on established RGBT tracking datasets. This work promotes a universal multimodal tracking throughout day and night.