Intelligent Intersection: Two-stream Convolutional Networks for Real-time Near-accident Detection in Traffic Video
Intelligent Intersection: Two-stream Convolutional Networks for Real-time Near-accident Detection in Traffic Video
复制标题
DOI：
10.1145/3373647
复制
发表时间：
2020-02-01
影响因子：
1.9
通讯作者：
Ranka, Sanjay
中科院分区：
文献类型：
--
作者：
Huang, Xiaohui;He, Pan;Ranka, Sanjay
基于摄像头的系统越来越多地用于收集路口和主干道的信息。与通常只能用于检测和移动车辆的环路控制器不同，摄像头可以提供有关交通行为的丰富信息。已经开发了用于多目标检测、目标跟踪和近身未命中检测的基于视觉的框架来获得该信息。然而，目前这项工作的很大一部分是处理离线视频。在本文中，我们提出了一种集成的双流卷积网络体系结构，用于对交通视频数据中的道路用户进行实时检测、跟踪和近事故检测。双流模型由用于目标检测的空间流网络和用于利用运动特征进行多目标跟踪的时间流网络组成。我们通过结合这两个网络的外观特征和运动特征来检测险情。此外，我们证明了我们的方法可以实时执行，并且在高于从鱼眼和头顶摄像头收集的各种视频的帧速率的帧速率下执行。
Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic behavior. Vision-based frameworks for multiple-object detection, object tracking, and near-miss detection have been developed to derive this information. However, much of this work currently addresses processing videos offline. In this article, we propose an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for object detection and a temporal stream network to leverage motion features for multiple-object tracking. We detect near-accidents by incorporating appearance features and motion features from these two networks. Further, we demonstrate that our approaches can be executed in real-time and at a frame rate that is higher than the video frame rate on a variety of videos collected from fisheye and overhead cameras.