Abstract
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world.
Abstract (translated)
以交通摄像机为传感器的城市交通优化正推动着先进的多目标多摄像机（MTMC）跟踪技术的发展。这项工作介绍了CityFlow，一个城市规模的交通摄像机数据集，它包含了从10个交叉口的40个摄像机中获取的超过3小时的同步高清视频，两个同步摄像机之间的最长距离为2.5公里。据我们所知，就空间覆盖率和城市环境中的摄像机/视频数量而言，CityFlow是最大规模的数据集。数据集包含超过200k个带注释的边界框，涵盖了各种场景、视角、车辆模型和城市交通流条件。提供相机几何和校准信息以帮助时空分析。此外，基准的一个子集可用于基于图像的车辆重新识别（REID）任务。我们对MTMC跟踪、多目标单相机跟踪、目标检测和基于图像的REID中的基线/最新方法进行了广泛的实验评估，分析了不同网络结构、损失函数、时空模型及其组合对任务有效性的影响。.随着我们在2019年人工智能城市挑战赛（https://www.ai city challenge.org/）上发布的基准测试，一个评估服务器启动，研究人员可以通过它来比较他们最新技术的性能。我们希望这个数据集能够促进这一领域的研究，推动最先进的技术进步，并在现实世界中实现部署的流量优化。
URL
https://arxiv.org/abs/1903.09254
