Multiple pedestrian tracking faces the challenge of tracking pedestrians in the presence of occlusion. Existing methods suffer from inaccurate motion estimation, appearance feature extraction, and association due to occlusion, leading to inadequate Identification F1-Score (IDF1), excessive ID switches (IDSw), and insufficient association accuracy and recall (AssA and AssR). We found that the main reason is abnormal detections caused by partial occlusion. In this paper, we suggest that the key insight is explicit motion estimation, reliable appearance features, and fair association in occlusion scenes. Specifically, we propose an adaptive occlusion-aware multiple pedestrian tracker, OccluTrack. We first introduce an abnormal motion suppression mechanism into the Kalman Filter to adaptively detect and suppress outlier motions caused by partial occlusion. Second, we propose a pose-guided re-ID module to extract discriminative part features for partially occluded pedestrians. Last, we design a new occlusion-aware association method towards fair IoU and appearance embedding distance measurement for occluded pedestrians. Extensive evaluation results demonstrate that our OccluTrack outperforms state-of-the-art methods on MOT-Challenge datasets. Particularly, the improvements on IDF1, IDSw, AssA, and AssR demonstrate the effectiveness of our OccluTrack on tracking and association performance.

翻译：多人跟踪任务面临遮挡场景中行人跟踪的挑战。现有方法因遮挡导致运动估计不准确、外观特征提取失效及关联错误，造成身份F1分数不足、身份切换过多以及关联准确率与召回率不达标。我们发现主要原因是部分遮挡引发的异常检测。本文认为关键在于遮挡场景中的显式运动估计、可靠外观特征与公平关联。为此，我们提出自适应遮挡感知的多行人跟踪器OccluTrack。首先，在卡尔曼滤波中引入异常运动抑制机制，自适应检测并抑制部分遮挡引起的离群运动；其次，设计姿态引导的重识别模块提取部分遮挡行人的判别性部件特征；最后，提出新颖的遮挡感知关联方法，实现遮挡行人的公平交并比与外观嵌入距离度量。大量实验结果表明，我们的OccluTrack在MOT-Challenge数据集上超越现有最优方法。其中IDF1、IDSw、AssA与AssR指标的提升充分验证了该方法在跟踪与关联性能上的有效性。