In this section
@article{Zhang:2022:10.1109/TITS.2022.3147770,
author = {Zhang, X and Feng, Y and Angeloudis, P and Demiris, Y},
doi = {10.1109/TITS.2022.3147770},
journal = {IEEE Transactions on Intelligent Transportation Systems},
pages = {14148--14165},
title = {Monocular visual traffic surveillance: a review},
url = {http://dx.doi.org/10.1109/TITS.2022.3147770},
volume = {23},
year = {2022}
}
TY - JOUR
AB - To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.
AU - Zhang,X
AU - Feng,Y
AU - Angeloudis,P
AU - Demiris,Y
DO - 10.1109/TITS.2022.3147770
EP - 14165
PY - 2022///
SN - 1524-9050
SP - 14148
TI - Monocular visual traffic surveillance: a review
T2 - IEEE Transactions on Intelligent Transportation Systems
UR - http://dx.doi.org/10.1109/TITS.2022.3147770
UR - http://hdl.handle.net/10044/1/97500
VL - 23
ER -
