Multi-Object Tracking (MOT) is a crucial computer vision task that aims to predict the bounding boxes and identities of objects simultaneously. While state-of-the-art methods have made remarkable progress by jointly optimizing the multi-task problems of detection and Re-ID feature learning, yet, few approaches explore to tackle the occlusion issue, which is a long-standing challenge in the MOT field. Generally, occluded objects may hinder the detector from estimating the bounding boxes, resulting in fragmented trajectories. And the learned occluded Re-ID embeddings are less distinct since they contain interferer. To this end, we propose an occlusion-aware detection and Re-ID calibrated network for multi-object tracking, termed as ORCTrack. Specifically, we propose an Occlusion-Aware Attention (OAA) module in the detector that highlights the object features while suppressing the occluded background regions. OAA can serve as a modulator that enhances the detector for some potentially occluded objects. Furthermore, we design a Re-ID embedding matching block based on the optimal transport problem, which focuses on enhancing and calibrating the Re-ID representations through different adjacent frames complementarily. To validate the effectiveness of the proposed method, extensive experiments are conducted on two challenging VisDrone2021-MOT and KITTI benchmarks. Experimental evaluations demonstrate the superiority of our approach, which can achieve new state-of-the-art performance and enjoy high run-time efficiency.

翻译：多目标跟踪（MOT）是一项关键的计算机视觉任务，旨在同时预测目标的边界框和身份信息。尽管现有最先进方法通过联合优化检测和重识别特征学习的多任务问题取得了显著进展，但很少有方法探索解决遮挡问题——这是MOT领域长期存在的挑战。通常，被遮挡的目标可能会阻碍检测器估计边界框，导致轨迹碎片化。同时，学习到的被遮挡重识别嵌入因包含干扰信息而区分度降低。为此，我们提出一种面向遮挡感知的多目标跟踪检测与重识别校准网络，命名为ORCTrack。具体而言，我们在检测器中设计了一个遮挡感知注意力（OAA）模块，该模块在抑制遮挡背景区域的同时突出目标特征。OAA可作为调制器增强检测器对潜在被遮挡目标的检测能力。此外，我们基于最优传输问题设计了重识别嵌入匹配模块，该模块通过不同相邻帧的互补信息专注于增强和校准重识别表示。为验证所提方法的有效性，我们在两个具有挑战性的基准数据集VisDrone2021-MOT和KITTI上进行了大量实验。实验评估表明，我们的方法具有优越性，能够实现新的最佳性能并保持高运行时效率。