论文标题
多相机轨迹预测：相机网络中的行人轨迹预测
Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras
论文作者
论文摘要
我们介绍了多相机轨迹预测（MCTF）的任务，其中对象在相机网络中预测了对象的未来轨迹。先前的作品考虑单个相机视图中的预测轨迹。我们的工作是第一个考虑跨多个非重叠摄像机视图的挑战性方案。这在重新识别和多目标多摄像机跟踪等任务中具有广泛的适用性。为了促进该新领域的研究，我们发布了Warwick-NTU多摄像机预测数据库（WNMF），这是一个独特的多相机步行轨迹数据集，该数据集从15个同步相机的网络中。为了准确标记这个大型数据集（600小时的视频录像），我们还开发了一种半自动注释方法。有效的MCTF模型应主动预测一个人将在摄像机网络中重新出现的位置和何时。在本文中，我们考虑了预测下一个相机的任务，这是行人在离开另一台相机后将重新出现的，并为此提供了几种基线方法。标记的数据库可在线获得：https：//github.com/olly-styles/multi-camera-trajectory-forecasting。
We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting trajectories in a single camera view. Our work is the first to consider the challenging scenario of forecasting across multiple non-overlapping camera views. This has wide applicability in tasks such as re-identification and multi-target multi-camera tracking. To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras. To accurately label this large dataset (600 hours of video footage), we also develop a semi-automated annotation method. An effective MCTF model should proactively anticipate where and when a person will re-appear in the camera network. In this paper, we consider the task of predicting the next camera a pedestrian will re-appear after leaving the view of another camera, and present several baseline approaches for this. The labeled database is available online: https://github.com/olly-styles/Multi-Camera-Trajectory-Forecasting.
