改进追踪算法，解决多人跟踪中的遮挡与运动误判问题。
MeMoSORT: Memory-Assisted Filtering and Motion-Adaptive Association Metric for Multi-Person Tracking
- 用记忆增强网络修正运动模型偏差
- 动态调整匹配范围并结合身高相似性提升精度
- 适合复杂场景下实时多人跟踪，尤其在遮挡多时表现优异
在以人为主的视频场景中，多人跟踪（MOT）因目标运动复杂和严重遮挡仍具挑战。传统基于检测的追踪方法依赖卡尔曼滤波（KF）和固定的交并比（IoU）匹配，前者运动模型常与实际不符导致滤波误差，后者在遮挡下易引发身份切换或目标丢失。为此，我们提出MeMoSORT，一种简单、在线、实时的MOT追踪器，包含两项创新：一是记忆增强卡尔曼滤波（MeKF），通过记忆增强神经网络补偿假设运动与真实运动间的差异；二是运动自适应交并比（Mo-IoU），自适应扩展匹配空间并引入高度相似性，降低检测误差和匹配失败的影响，同时保持轻量。在DanceTrack和SportsMOT数据集上的实验表明，MeMoSORT分别取得67.9%和82.1%的HOTA分数，达到当前最优性能。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) in human-dominant scenarios, which involves continuously tracking multiple people within video sequences, remains a significant challenge in computer vision due to targets' complex motion and severe occlusions. Conventional tracking-by-detection methods are fundamentally limited by their reliance on Kalman filter (KF) and rigid Intersection over Union (IoU)-based association. The motion model in KF often mismatches real-world object dynamics, causing filtering errors, while rigid association struggles under occlusions, leading to identity switches or target loss. To address these issues, we propose MeMoSORT, a simple, online, and real-time MOT tracker with two key innovations. First, the Memory-assisted Kalman filter (MeKF) uses memory-augmented neural networks to compensate for mismatches between assumed and actual object motion. Second, the Motion-adaptive IoU (Mo-IoU) adaptively expands the matching space and incorporates height similarity to reduce the influence of detection errors and association failures, while remaining lightweight. Experiments on DanceTrack and SportsMOT show that MeMoSORT achieves state-of-the-art performance, with HOTA scores of 67.9\% and 82.1\%, respectively.
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