Open AccessFast video‐based queue length detection approach for self‐organising traffic control
Author(s) -
Jiang Tao,
Cai Mingdai,
Zhang Yulong,
Jia Xiaojie
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5073
Subject(s) - queue , computer science , real time computing , adaboost , queueing theory , traffic congestion , key (lock) , artificial intelligence , simulation , computer vision , classifier (uml) , engineering , computer network , transport engineering , computer security
With the development of traffic control theory, self‐organising control has become one of the most promising techniques for traffic control. Among the various parameters of self‐organising control, the queue length (number of vehicles) is a key one. Compared to the approaches using other detectors, vision‐based approaches have a greater potential in queue length detection. However, the complicated traffic conditions (such as the changeable weather and the vehicle occlusion) and the large calculation of image processing have posed great challenges to the real‐time application. Here, a fast video‐based queue length detection approach is proposed. For each lane, according to the level of traffic congestion evaluated by the foreground area ratio, the corresponding vehicle counting method is adopted. When the traffic is determined congested, a fast area‐based method for estimating the number of vehicles is adopted. Otherwise, a fast vehicle detection and counting method combining background difference and Adaboost classifier is adopted. The experiment results demonstrate that the proposed approach can handle actual traffic environment and has great potential for real‐time application.