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

• URL: http://arxiv.org/abs/2502.16809v1
• Date: Mon, 24 Feb 2025 03:35:38 GMT
• Title: CRTrack: Low-Light Semi-Supervised Multi-object Tracking Based on Consistency Regularization
• Authors: Zijing Zhao, Jianlong Yu, Lin Zhang, Shunli Zhang,
• Abstract summary: We propose a semi-supervised multi-object tracking method based on consistency regularization named CRTrack.<n>First, we calibrate a consistent adaptive sampling assignment to replace the static IoU-based strategy.<n>Then, we design a adaptive semi-supervised network update method, which effectively leverages unannotated data to enhance model performance.
• Score: 9.878424965835883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking under low-light environments is prevalent in real life. Recent years have seen rapid development in the field of multi-object tracking. However, due to the lack of datasets and the high cost of annotations, multi-object tracking under low-light environments remains a persistent challenge. In this paper, we focus on multi-object tracking under low-light conditions. To address the issues of limited data and the lack of dataset, we first constructed a low-light multi-object tracking dataset (LLMOT). This dataset comprises data from MOT17 that has been enhanced for nighttime conditions as well as multiple unannotated low-light videos. Subsequently, to tackle the high annotation costs and address the issue of image quality degradation, we propose a semi-supervised multi-object tracking method based on consistency regularization named CRTrack. First, we calibrate a consistent adaptive sampling assignment to replace the static IoU-based strategy, enabling the semi-supervised tracking method to resist noisy pseudo-bounding boxes. Then, we design a adaptive semi-supervised network update method, which effectively leverages unannotated data to enhance model performance. Dataset and Code: https://github.com/ZJZhao123/CRTrack.

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