提出CRTrack方法，在低光环境下用少量标注数据实现多目标跟踪。
CRTrack: Low-Light Semi-Supervised Multi-object Tracking Based on Consistency Regularization
- 用一致性正则化实现半监督学习，降低标注成本。
- 自适应采样分配提升对噪声伪框的鲁棒性。
- 构建了首个低光多目标追踪数据集LLMOT，适合视觉算法研究者。
低光环境下的多目标跟踪在实际应用中十分常见。近年来多目标跟踪领域发展迅速，但由于缺乏数据集且标注成本高，低光条件下的跟踪仍面临持续挑战。本文聚焦低光环境下的多目标跟踪问题，首先构建了低光多目标跟踪数据集LLMOT，包含经夜间增强的MOT17数据及多个未标注低光视频。为应对标注成本高和图像质量下降问题，提出基于一致性正则化的半监督跟踪方法CRTrack。首先，设计一致性的自适应采样分配策略，替代静态的IoU策略，有效抵抗噪声伪边界框；其次，提出自适应半监督网络更新机制，充分挖掘未标注数据以提升模型性能。代码与数据集已开源。
原文摘要 · Abstract (English)
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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