论文标题
实时3D深度摄像机跟踪
Real-time 3D Deep Multi-Camera Tracking
论文作者
论文摘要
使用多个RGB摄像机在3D中跟踪人群是一项具有挑战性的任务。大多数以前的多相机跟踪算法都是为离线设置而设计的，并且具有较高的计算复杂性。强大的实时多相机3D跟踪仍然是一个未解决的问题。在这项工作中，我们提出了一条新颖的端到端跟踪管道，深度摄像机跟踪（DMCT），可实现可靠的实时多摄像机人员跟踪。我们的DMCT包括1）一个快速而新颖的视角 - 意识到的深度groudpoint网络，2）地面占用热图估计的融合程序，3）一个新颖的人检测深度瞥见网络和4）快速准确的在线跟踪器。我们的设计完全释放了深神经网络的功能，以估计每个颜色图像中每个人的“接地点”，可以优化以有效，稳健的运行。我们的融合过程，瞥见网络和跟踪器从不同视图中合并了结果，使用多个视频帧找到候选人，然后跟踪融合热图上的人们。我们的系统在保持实时性能的同时，可以实现最新的跟踪结果。除了对挑战性的Wildtrack数据集进行评估外，我们还收集了来自两个不同环境和相机设置的高质量标签的另外两个跟踪数据集。我们的实验结果证实，我们提出的实时管道比以前的方法给出了较好的结果。
Tracking a crowd in 3D using multiple RGB cameras is a challenging task. Most previous multi-camera tracking algorithms are designed for offline setting and have high computational complexity. Robust real-time multi-camera 3D tracking is still an unsolved problem. In this work, we propose a novel end-to-end tracking pipeline, Deep Multi-Camera Tracking (DMCT), which achieves reliable real-time multi-camera people tracking. Our DMCT consists of 1) a fast and novel perspective-aware Deep GroudPoint Network, 2) a fusion procedure for ground-plane occupancy heatmap estimation, 3) a novel Deep Glimpse Network for person detection and 4) a fast and accurate online tracker. Our design fully unleashes the power of deep neural network to estimate the "ground point" of each person in each color image, which can be optimized to run efficiently and robustly. Our fusion procedure, glimpse network and tracker merge the results from different views, find people candidates using multiple video frames and then track people on the fused heatmap. Our system achieves the state-of-the-art tracking results while maintaining real-time performance. Apart from evaluation on the challenging WILDTRACK dataset, we also collect two more tracking datasets with high-quality labels from two different environments and camera settings. Our experimental results confirm that our proposed real-time pipeline gives superior results to previous approaches.
