@inproceedings{a066f3e5fc3f420d8ee229ba1f83dba0,
title = "Traffic congestion analysis: A new Perspective",
abstract = "In this paper, a new perspective of congestion is presented to promote the development of traffic video analysis. Our main contributions are threefold: a) An unified and quantifiable definition of congestion is proposed to describe the traffic state in video. b) Based on the definition, a congestion dataset which contains multiple traffic scenes is constructed as a platform for the research community. At the same time, a precise labeling method is introduced to get the ground truth of congestion level accurately. c) An algorithm based on Inverse Perspective Mapping (IPM) and pairwise regression is proposed to analyze traffic videos and serves as a baseline. We further compare the proposed method with two deep learning methods. Intensive experiments justify the effectiveness of the proposed method.",
keywords = "congestion detection, image understanding, signal processing, Traffic video",
author = "Jia Wan and Yuan Yuan and Qi Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952386",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1398--1402",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}
