A Deep Learning Framework for Video-Based Vehicle Counting
A Deep Learning Framework for Video-Based Vehicle Counting
复制标题
基于视频的车辆计数深度学习框架
DOI：
10.3389/fphy.2022.829734
复制
发表时间：
2022-02-21
影响因子：
3.1
通讯作者：
Guo, Renzhong
中科院分区：
文献类型：
--
作者：
Lin, Haojia;Yuan, Zhilu;Guo, Renzhong
交通监测可用于监测和收集道路网络的交通状况数据，这在智能交通系统（ITS）的广泛应用中发挥着重要作用。准确、快速地检测和统计交通视频中的车辆是交通监测的主要问题之一。传统的基于视频的车辆检测方法，如背景减除、帧差法和光流法，在准确性或效率方面存在一些局限性。本文将深度学习应用于交通视频中的车辆计数。首先，为解决标注数据匮乏的问题，提出一种基于迁移学习的车辆检测方法。然后，在车辆检测的基础上，提出一种融合虚拟检测区域与车辆跟踪的车辆计数方法。最后，鉴于可能出现漏检和误检的情况，设计了漏报抑制模块和误报抑制模块，以提高车辆计数的准确性。结果表明，所提出的深度学习车辆计数框架在标注数据不足的情况下能够实现车道级别的车辆计数，车辆计数准确率最高可达99%。在计算效率方面，该方法具有较高的实时性，可用于实时车辆计数。
Traffic surveillance can be used to monitor and collect the traffic condition data of road networks, which plays an important role in a wide range of applications in intelligent transportation systems (ITSs). Accurately and rapidly detecting and counting vehicles in traffic videos is one of the main problems of traffic surveillance. Traditional video-based vehicle detection methods, such as background subtraction, frame difference, and optical flow have some limitations in accuracy or efficiency. In this paper, deep learning is applied for vehicle counting in traffic videos. First, to solve the problem of the lack of annotated data, a method for vehicle detection based on transfer learning is proposed. Then, based on vehicle detection, a vehicle counting method based on fusing the virtual detection area and vehicle tracking is proposed. Finally, due to possible situations of missing detection and false detection, a missing alarm suppression module and a false alarm suppression module are designed to improve the accuracy of vehicle counting. The results show that the proposed deep learning vehicle counting framework can achieve lane-level vehicle counting without enough annotated data, and the accuracy of vehicle counting can reach up to 99%. In terms of computational efficiency, this method has high real-time performance and can be used for real-time vehicle counting.