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Traffic Flow Video Image Recognition and Analysis Based on Multi-Target Tracking Algorithm and Deep Learning

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成果类型:
期刊论文
作者:
Zou, Songshang;Chen, Hao;Feng, Hui;Xiao, Guangyi;Qin, Zhen;...
通讯作者:
Chen, H.
作者机构:
[Zou, Songshang; Xiao, Guangyi; Qin, Zhen; Chen, Hao] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China.
[Feng, Hui] Hainan Vocat Univ Sci & Technol, Sch Informat Engn, Haikou 571126, Peoples R China.
[Feng, Hui] Jiangsu Vocat & Tech Coll Finance & Econ, Sch Intelligent Engn Technol, Huaian 223003, Peoples R China.
[Cai, Weiwei] Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha 410004, Peoples R China.
[Cai, Weiwei] AiTech Artificial Intelligence Res Inst, Changsha 410000, Peoples R China.
通讯机构:
[Chen, H.] H
Hunan University, China
语种:
英文
关键词:
estimation model;feature recognition;LSTM;multi-target tracking;Traffic flow real-time detection
期刊:
IEEE Transactions on Intelligent Transportation Systems
ISSN:
1524-9050
年:
2023
卷:
24
期:
8
页码:
8762-8775
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62272156 and U20A20174) Science and Technology Projects of Hunan Province (Grant Number: 2019WK2072, 2018TP2023 and 2015TP1004)
机构署名:
本校为其他机构
院系归属:
交通运输与物流学院
摘要:
Traffic flow parameters are an important data support for the research and development of several technologies in the intelligent transportation system. Therefore, accurate and real-time estimation of traffic flow is particularly important for urban traffic. In this study, a real-time traffic flow detection system framework was constructed based on video image collection and analysis. According to the vehicle detection and tracking results, a traffic flow parameter estimation model and an improved LSTM network are proposed for spatiotemporal counting feature recognition. The results conclude t...

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