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。

暗光下的多目标追踪

Multi-Object Tracking in the Dark

Understanding dark scenes based on multi-modal image data is challenging, as
both the visible and auxiliary modalities provide limited semantic information
for the task. Previous methods focus on fusing the two modalities but neglect
the correlations among semantic classes when minimizing losses to align pixels
with labels, resulting in inaccurate class predictions. To address these
issues, we introduce a supervised multi-modal contrastive learning approach to
increase the semantic discriminability of the learned multi-modal feature
spaces by jointly performing cross-modal and intra-modal contrast under the
supervision of the class correlations. The cross-modal contrast encourages
same-class embeddings from across the two modalities to be closer and pushes
different-class ones apart. The intra-modal contrast forces same-class or
different-class embeddings within each modality to be together or apart. We
validate our approach on a variety of tasks that cover diverse light conditions
and image modalities. Experiments show that our approach can effectively
enhance dark scene understanding based on multi-modal images with limited
semantics by shaping semantic-discriminative feature spaces. Comparisons with
previous methods demonstrate our state-of-the-art performance. Code and
pretrained models are available at this https URL

通过多模态图像数据，采用监督的多模态对比学习方法，增加学习到的多模态特征空间的语义可辨别性，从而提高对黑暗场景的理解。实验证明该方法能够有效增强基于有限语义的多模态图像的语义区分特征空间，并展示了其与先前方法相比的卓越性能。