Low-light scenes are prevalent in real-world applications (e.g. autonomous
driving and surveillance at night). Recently, multi-object tracking in various
practical use cases have received much attention, but multi-object tracking in
dark scenes is rarely considered. In this paper, we focus on multi-object
tracking in dark scenes. To address the lack of datasets, we first build a
Low-light Multi-Object Tracking (LMOT) dataset. LMOT provides well-aligned
low-light video pairs captured by our dual-camera system, and high-quality
multi-object tracking annotations for all videos. Then, we propose a low-light
multi-object tracking method, termed as LTrack. We introduce the adaptive
low-pass downsample module to enhance low-frequency components of images
outside the sensor noises. The degradation suppression learning strategy
enables the model to learn invariant information under noise disturbance and
image quality degradation. These components improve the robustness of
multi-object tracking in dark scenes. We conducted a comprehensive analysis of
our LMOT dataset and proposed LTrack. Experimental results demonstrate the
superiority of the proposed method and its competitiveness in real night
low-light scenes. Dataset and Code: https: //github.com/ying-fu/LMOT

本文介绍了一个低光场景下的多目标跟踪方法和相应的数据集 LMOT，证实了该方法在实际夜间低光场景中的竞争力。