Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance with crowded scenes or in vast space. In this work, we propose a mathematically elegant multi-camera multiple object tracking approach based on a spatial-temporal lifted multicut formulation. Our model utilizes state-of-the-art tracklets produced by single-camera trackers as proposals. As these tracklets may contain ID-Switch errors, we refine them through a novel pre-clustering obtained from 3D geometry projections. As a result, we derive a better tracking graph without ID switches and more precise affinity costs for the data association phase. Tracklets are then matched to multi-camera trajectories by solving a global lifted multicut formulation that incorporates short and long-range temporal interactions on tracklets located in the same camera as well as inter-camera ones. Experimental results on the WildTrack dataset yield near-perfect result, outperforming state-of-the-art trackers on Campus while being on par on the PETS-09 dataset. We will make our implementations available upon acceptance of the paper.
翻译:多卡梅拉多目标跟踪目前正在计算机视觉领域引起人们的注意,因为它在现实世界应用(如超拥挤的场景或广阔的空间的视频监视)中的超强性能,因此在计算机视觉领域引起人们的注意。在这项工作中,我们提议了一种数学优雅的多镜头多对象跟踪方法,其基础是空间时空拆解多截面配方。我们的模型使用由单个摄像头追踪器作为建议产生的最先进的跟踪跟踪器。由于这些跟踪器可能包含ID-开关错误,我们通过从3D几何学预测中获得的新颖的组合前结果加以改进。结果,我们得出了一个更好的跟踪图,没有ID开关,也没有数据组合阶段的精确亲近性成本。然后,我们提出一个数学优美的多镜头跟踪器与多镜头轨迹相匹配,方法是解决一个全球升开的多截面配方,将位于同一摄像头的轨迹上的短长时间互动和相隔开的跟踪器作为建议。由于这些跟踪器的实验结果,因此,我们从3D几何测测图中得出了近的超速结果。结果,在校园上的状态跟踪仪上表现优异。我们将在PET-09号的页面上得到接受。我们将在PET。