通过时空关联建模提升无人机视频多目标跟踪精度
STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking
- 利用历史特征序列建模目标时空关联性
- 在VisDrone2019和UAVDT上达到SOTA的MOTA和IDF1
- 适合处理形变、模糊等复杂场景的跟踪任务
无人机视频中的多目标跟踪在计算机视觉诸多应用中具有重要意义。现有跟踪器依赖精确的目标检测结果和目标重识别（ReID）匹配，通常只优化目标的空间属性，忽视了建模目标关系时的时序线索，尤其在目标形变、模糊等挑战性条件下表现不佳。为此，本文提出一种新颖的时空一致性多目标跟踪框架（STCMOT），通过历史嵌入特征按时间顺序建模ReID与检测特征的表示。具体地，引入时序嵌入增强模块，基于相邻帧协作提升单个嵌入的区分度；同时，通过时序检测精炼模块传播轨迹嵌入，在时序域中挖掘显著目标位置。在VisDrone2019和UAVDT数据集上的大量实验表明，STCMOT在MOTA和IDF1指标上均达到新最优水平。代码已开源：https://github.com/ydhcg-BoBo/STCMOT。
原文摘要 · Abstract (English)
Multiple object tracking (MOT) in Unmanned Aerial Vehicle (UAV) videos is important for diverse applications in computer vision. Current MOT trackers rely on accurate object detection results and precise matching of target reidentification (ReID). These methods focus on optimizing target spatial attributes while overlooking temporal cues in modelling object relationships, especially for challenging tracking conditions such as object deformation and blurring, etc. To address the above-mentioned issues, we propose a novel Spatio-Temporal Cohesion Multiple Object Tracking framework (STCMOT), which utilizes historical embedding features to model the representation of ReID and detection features in a sequential order. Concretely, a temporal embedding boosting module is introduced to enhance the discriminability of individual embedding based on adjacent frame cooperation. While the trajectory embedding is then propagated by a temporal detection refinement module to mine salient target locations in the temporal field. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate our STCMOT sets a new state-of-the-art performance in MOTA and IDF1 metrics. The source codes are released at https://github.com/ydhcg-BoBo/STCMOT.
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