DOA-SORT：基于分布观测的方向感知遮挡多目标跟踪

中文摘要 AI 辅助

多目标跟踪（MOT）中的身份关联容易受到部分遮挡、截断检测和置信度分数波动的影响。现有的以运动为主的跟踪器通常将遮挡表示为标量惩罚。这种处理方式忽略了遮挡造成的方向性观测偏差：左、右、上、下遮挡会以不同方式扭曲检测的位置和形状。我们提出了\ours{}（方向感知遮挡SORT），一种在线且无需训练的跟踪器，显式地建模这些偏差。首先，它从边界框重叠和相对底部位置推断出软性的前后排序，并估计方向性遮挡覆盖率和深度。然后，它构建一个由一个干净分量和四个方向性遮挡观测分量组成的混合模型。该模型使用包含框中心、面积、置信度和长宽比的五维观测，并根据预测的遮挡和检测置信度自适应调整观测噪声。方向性混合似然用于高置信度关联、低置信度关联和轨迹恢复；歧义惩罚和局部顺序一致性交换进一步减少了附近物体之间的身份错误。在DanceTrack验证集上，与使用相同检测器和评估协议的OA-SORT相比，\ours{}将HOTA从63.00提高到66.34，AssA从45.10提高到49.57，IDF1从62.19提高到65.28。增益主要集中在关联质量上，而检测精度保持稳定。在MOT17和MOT20训练集上的额外局部评估表征了在相同无ReID跟踪协议下的跨数据集行为。

英文摘要

Identity association in multi-object tracking (MOT) is vulnerable to partial occlusion, truncated detections, and fluctuating confidence scores. Existing motion-dominant trackers commonly represent occlusion as a scalar penalty. This treatment misses the directional observation bias caused by occlusion: left, right, top, and bottom occlusions distort the location and shape of a detection in different ways. We propose \ours{} (Directional Occlusion-Aware SORT), an online and training-free tracker that models these biases explicitly. First, it infers a soft front--back ordering from box overlap and relative bottom positions, and estimates directional occlusion coverage and depth. It then constructs a mixture of one clean and four directional occlusion observation components. The model uses a five-dimensional observation comprising box center, area, confidence, and aspect ratio, and adapts observation noise to predicted occlusion and detection confidence. The directional mixture likelihood is used in high-confidence association, low-confidence association, and track recovery; ambiguity penalties and local order-consistency swaps further reduce identity errors among nearby objects. On the DanceTrack validation split, \ours{} improves HOTA from 63.00 to 66.34, AssA from 45.10 to 49.57, and IDF1 from 62.19 to 65.28 over OA-SORT with the same detector and evaluation protocol. The gains are concentrated in association quality while detection accuracy remains stable. Additional local evaluations on MOT17 and MOT20 train splits characterize cross-dataset behavior under the same no-ReID tracking protocol.

发表机构

• BDNRC

机构由 AI 辅助整理，请以论文原文为准。