Premium
Recognition method of traffic violations based on complex interaction between multiple entities
Author(s) -
Shen Xiaohu,
An Jubai,
Teng Zhisong
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22511
Subject(s) - computer science , set (abstract data type) , feature (linguistics) , baseline (sea) , artificial intelligence , pedestrian , background subtraction , data mining , filter (signal processing) , pattern recognition (psychology) , machine learning , computer vision , engineering , pixel , philosophy , linguistics , oceanography , transport engineering , programming language , geology
Existing methods used in detecting vehicles with traffic violations are mostly based on single‐entity frameworks, and those involving multientities remain to be limited. In this paper, we propose a traffic violation detection model, an intelligent vehicle violation recognition method based on multiple entities. It aims to identify vehicles violating the rule of yielding to pedestrians at nonsignalized crosswalks. First, we define the concepts of vehicles and pedestrians and then apply the regression background subtraction method and particle filter algorithm to automatically identify and track moving objects. Second, the pedestrian validity feature template is specified to detect temporal trajectory features from videos with labels and to train classification networks aiming to identify unreasonable behavior patterns, such as remaining on the sidewalk, entering the roadway, bicycle riding, and others. Finally, we develop a novel traffic violation recognition method based on multientity interaction analysis. The cases of failing to yield to pedestrians are recognized based on the multientity feature template built using the proposed method. We verified the effectiveness of the proposed method on a real traffic data set obtained from surveillance cameras. The obtained results are significantly better compared with the baseline method. The area under curve value of the proposed traffic violation recognition method with multientity interaction is 14.5%, 11.1%, and 6.6% higher compared with the three baseline methods based on single‐entity frameworks.