A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Wang, Zhiyuan, Huang, Jifeng, Xiong, Neal N., Zhou, Xiaoping, Lin, Xiao, Ward, Theodore Lee
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Datasets; Feature extraction; H-SqueezeNet; intelligent traffic surveillance system; Intelligent vehicles; MOG2; Real-time systems; Robustness; Smart cities; smart city; Subtraction; Surveillance; Surveillance systems; Traffic control; Traffic information; Traffic speed; Traffic surveillance; Vehicle detection; Video data
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3012995

Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.