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博士生林浩嘉,郭仁忠的论文在FRONTIERS IN PHYSICS刊出
发布时间:2022-06-24 10:23:36     发布者:易真     浏览次数:

标题: A Deep Learning Framework for Video-Based Vehicle Counting

作者: Lin, HJ (Lin, Haojia); Yuan, ZL (Yuan, Zhilu); He, B (He, Biao); Kuai, X (Kuai, Xi); Li, XM (Li, Xiaoming); Guo, RZ (Guo, Renzhong)

来源出版物: FRONTIERS IN PHYSICS : 10 文献号: 829734 DOI: 10.3389/fphy.2022.829734 出版年: FEB 21 2022

摘要: 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.

作者关键词: intelligent transportation systems; traffic video; vehicle detection; vehicle counting; deep learning

地址: [Lin, Haojia; Guo, Renzhong] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Lin, Haojia; Yuan, Zhilu; He, Biao; Kuai, Xi; Li, Xiaoming; Guo, Renzhong] Shenzhen Univ, Guangdong Hong Kong Macau Joint Lab Smart Cities, Sch Architecture & Urban Planning, Shenzhen, Peoples R China.

[Lin, Haojia; Yuan, Zhilu; He, Biao; Kuai, Xi; Li, Xiaoming; Guo, Renzhong] Shenzhen Univ, Shenzhen Key Lab Digital Twin Technol Cities, Sch Architecture & Urban Planning, Shenzhen, Peoples R China.

[Lin, Haojia; Yuan, Zhilu; He, Biao; Kuai, Xi; Li, Xiaoming; Guo, Renzhong] Shenzhen Univ, Res Inst Smart Cities, Sch Architecture & Urban Planning, Shenzhen, Peoples R China.

[He, Biao] MNR Technol Innovat Ctr Terr & Spatial Big Data, Shenzhen, Peoples R China.

[He, Biao] MNR Key Lab Urban Land Resources Monitoring & Sim, Shenzhen, Peoples R China.

通讯作者地址: Guo, RZ (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

Guo, RZ (通讯作者)Shenzhen Univ, Guangdong Hong Kong Macau Joint Lab Smart Cities, Sch Architecture & Urban Planning, Shenzhen, Peoples R China.

Guo, RZ (通讯作者)Shenzhen Univ, Shenzhen Key Lab Digital Twin Technol Cities, Sch Architecture & Urban Planning, Shenzhen, Peoples R China.

Guo, RZ (通讯作者)Shenzhen Univ, Res Inst Smart Cities, Sch Architecture & Urban Planning, Shenzhen, Peoples R China.

电子邮件地址: guorz@szu.edu.cn

影响因子:3.56


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