论文标题
带有遮挡处理的多视图3D多对象跟踪的贝叶斯过滤器
A Bayesian Filter for Multi-view 3D Multi-object Tracking with Occlusion Handling
论文作者
论文摘要
本文提出了一个在线多摄像机多对象跟踪器，该跟踪器仅需要单眼探测器训练，而与多摄像机配置无关，可以无缝扩展/删除相机而无需重新训练。所提出的算法在整个相机的检测总数中具有线性复杂性，因此，相机数量优雅。它在3D世界框架中运行，并提供对象的3D轨迹估计。关键创新是一个高保真且可拖动的3D遮挡模型，可与最佳的贝叶斯多视图多对象过滤相提并论，该模型将无缝集成到单个贝叶斯递归中，这是轨道管理，状态估计，混乱抑制和遮挡/误解/误解的子手术的子任务。该算法在最新的Wildtracks数据集上进行了评估，并证明可以在新数据集中非常拥挤的场景中使用。
This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset.
