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.
Monocular Visual Traffic Surveillance: A Review
IEEE Transactions on Intelligent Transportation Systems ; 23 , 9 ; 14148-14165
2022-09-01
4731263 byte
Article (Journal)
Electronic Resource
English
Implementation of an Approach for 3D Vehicle Detection in Monocular Traffic Surveillance Videos
TIBKAT | 2021
|The monocular visual imaging technology model applied in the airport surface surveillance [8908-3]
British Library Conference Proceedings | 2013
|Visual traffic surveillance framework: classification to event detection
British Library Online Contents | 2013
|