CRTrack: Low-Light Semi-Supervised Multi-Object Tracking Based on Consistency Regularization

Multi-object tracking (MOT) in low-light environments presents significant real-world application value. Despite substantial progress in MOT, low-light MOT remains constrained by the scarcity of specialized datasets, largely because collecting and manually annotating low-light tracking data is difficult and often prohibitively expensive. This paper specifically addresses these challenges through methodological and dataset innovations. We first present low-light multi-object tracking (LLMOT), the first comprehensive low-light MOT dataset containing 11,580 images, including 5316 labeled and 6264 unlabeled images, consisting of nighttime-enhanced MOT17 sequences and multiple unannotated low-light videos. To simultaneously alleviate the constraint of annotation costs and address the damage that low-light-induced image degradation causes to pseudo-label quality, we propose Consistency Regularization Track (CRTrack), a semi-supervised framework tailored for low-light scenarios. Specifically, we introduce a consistent adaptive sampling assignment mechanism that calibrates and filters noisy and shifted pseudo-bounding boxes under low-illumination conditions. We then design an adaptive semi-supervised network update strategy that enables the model to more stably exploit unlabeled low-light videos for iterative optimization. Extensive experiments on the LLMOT dataset validate the effectiveness and robustness of the proposed method. CRTrack achieves 62.472 HOTA, 71.544 MOTA, and 75.864 IDF1 on the LLMOT dataset, demonstrating its effectiveness in low-light MOT. Our approach provides a practical solution for low-light MOT tasks with significant real-world implications.