用伪深度信息提升复杂遮挡下的多目标追踪精度
PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues
- 引入伪深度状态和深度体积IoU增强关联判断
- 在DanceTrack等数据集上显著提升追踪准确率
- 适合处理遮挡严重、运动复杂的实际场景
多目标追踪（MOT）是视频处理中的重要方向，在消费电子领域有广泛应用。当前主流的检测后追踪（TBD）方法在复杂场景下因遮挡导致关联性能下降，限制了实际应用。为此，本文提出伪深度SORT（PD-SORT），通过扩展卡尔曼滤波状态向量引入伪深度信息，设计结合2D IoU与伪深度的深度体积IoU（DVIoU），并提出量化伪深度测量（QPDM）策略以增强关联鲁棒性。同时集成相机运动补偿（CMC）应对动态相机情况。实验表明，该方法显著缓解遮挡引起的关联歧义，在DanceTrack、MOT17、MOT20上均取得领先性能，尤其在物体运动复杂、外观相似且频繁遮挡的DanceTrack上提升明显。代码已开源。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) is the dominant paradigm for MOT, which performs target detection and association frame by frame. However, the association performance of TBD methods degrades in complex scenes with heavy occlusions, which hinders the application of such methods in real-world scenarios.To this end, we incorporate pseudo-depth cues to enhance the association performance and propose Pseudo-Depth SORT (PD-SORT). First, we extend the Kalman filter state vector with pseudo-depth states. Second, we introduce a novel depth volume IoU (DVIoU) by combining the conventional 2D IoU with pseudo-depth. Furthermore, we develop a quantized pseudo-depth measurement (QPDM) strategy for more robust data association. Besides, we also integrate camera motion compensation (CMC) to handle dynamic camera situations. With the above designs, PD-SORT significantly alleviates the occlusion-induced ambiguous associations and achieves leading performances on DanceTrack, MOT17, and MOT20. Note that the improvement is especially obvious on DanceTrack, where objects show complex motions, similar appearances, and frequent occlusions. The code is available at https://github.com/Wangyc2000/PD_SORT.
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