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首页> 外文会议>IEEE International Conference on Intelligent Transportation Systems >Link Speed Estimation for Traffic Flow Modelling Based on Video Feeds from Monocular Cameras
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Link Speed Estimation for Traffic Flow Modelling Based on Video Feeds from Monocular Cameras

机译：基于单手套摄像机的视频源的交通流模型链路速度估计

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In this paper, we present a reliable and scalable approach for real-time estimation of link speeds (i.e., traffic speeds on specific road segments) based on video feeds coming from monocular cameras. We detect and track vehicles of specific types, identify anchor points (or keypoints) on them, compute their poses, and use this information to estimate their speeds. We use deep learning methods for vehicle detection, tracking, keypoint detection and localization, and traditional 3D pose estimation techniques for which precise mathematical solutions are available. Thus, our approach exploits the best of both worlds. The proposed approach does not require any physical measurements (extrinsics) in the road scene, making it scalable and easy to install. Our results on video feeds from Bangalore, India, show that the method is able to generalize well for cameras mounted on street light poles, congested traffic situations, and various lighting conditions. Thus, the solution is suitable for emerging market scenarios where traffic tends to be chaotic and dense, and mounting speed sensors or strategically located downward-facing cameras is not feasible. The code and dataset for this work are being made available2.2https://github.com/ShantamShorewala/vehicle-speed-keypoint-data
机译：在本文中，我们基于来自单眼摄像机的视频馈送的视频馈送来介绍一种可靠和可扩展的链路速度估计（即，特定路段上的交通速度）。我们检测到特定类型的车辆，识别它们上的锚点（或关键点），计算它们的姿势，并使用此信息来估计其速度。我们使用深度学习方法进行车辆检测，跟踪，关键点检测和定位，以及可用的传统3D姿势估计技术，可提供精确的数学解决方案。因此，我们的方法利用了两个世界的佼佼者。所提出的方法不需要道路场景中的任何物理测量（外在），使其可扩展且易于安装。我们的搜索结果来自印度班加罗尔的视频饲料，表明该方法能够概括安装在街道灯极，拥挤的交通情况和各种照明条件上的摄像机。因此，该解决方案适用于新兴市场情景，其中交通往往是混沌和密集的，并且安装速度传感器或策略性地位于面向下的相机是不可行的。此工作的代码和数据集正在提供可用 2 。 2 https://github.com/shantamshorewala/vehicle-speed-keypoint-data.

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