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

Abstract

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.

Object Detection in Traffic Videos: A Survey

“…Lightweight networks such as SqueezeNet [155], MobileNet [156], and ShuffleNet [157] can be applied to detect objects on edge devices. With the development of smart cities, several studies have attempted to take advantage of the more efficient models to bring advanced AI models to the edge for bandwidth and privacy optimization in traffic surveillance [131], [145], [158], [159]. Highway and urban traffic [165] Obtaining vehicle information Lucking et al [146] 2020 SSD 2D Urban traffic Vehicle counting on edge device Bui et al [142] 2020 YOLOv3 2D 2019 AI City Challenge [41] Vehicle counting Li et al [59] 2020 Faster R-CNN 2D 2020 AI City Challenge [166] Unsupervised anomaly detection Zheng [126] 2020 Mask R-CNN 2D CityFlow dataset [167] Vehicle re-identification Revaud and Humenberger [130] 2021 Mask R-CNN 3D BrnoCompSpeed dataset [160] Speed estimation Zhang [168] 2021 YOLCAT++ 2D Urban intersections (China) Pedestrian and sidewalk detection Chen et al [ The performance of the object detection methods based on handcrafted features could not satisfy the requirements of many real-world applications.…”

Object Detection in Traffic Videos: A Survey

“…Lightweight networks such as SqueezeNet [155], MobileNet [156], and ShuffleNet [157] can be applied to detect objects on edge devices. With the development of smart cities, several studies have attempted to take advantage of the more efficient models to bring advanced AI models to the edge for bandwidth and privacy optimization in traffic surveillance [131], [145], [158], [159]. Highway and urban traffic [165] Obtaining vehicle information Lucking et al [146] 2020 SSD 2D Urban traffic Vehicle counting on edge device Bui et al [142] 2020 YOLOv3 2D 2019 AI City Challenge [41] Vehicle counting Li et al [59] 2020 Faster R-CNN 2D 2020 AI City Challenge [166] Unsupervised anomaly detection Zheng [126] 2020 Mask R-CNN 2D CityFlow dataset [167] Vehicle re-identification Revaud and Humenberger [130] 2021 Mask R-CNN 3D BrnoCompSpeed dataset [160] Speed estimation Zhang [168] 2021 YOLCAT++ 2D Urban intersections (China) Pedestrian and sidewalk detection Chen et al [ The performance of the object detection methods based on handcrafted features could not satisfy the requirements of many real-world applications.…”

“…Transportation departments and transit agencies are interested in computer vision techniques that can process multiple video streams simultaneously without violating the strict real-time requirements. As a result, lightweight object detectors have gained popularity among modern traffic surveillance systems [131], [145], [158], [159].…”

Research on operation perception and monitoring for urban underground roads in Shanghai, China

“…The equipment and systems of video surveillance have widely used into intelligent traffic system, which greatly meeting the needs of real-time, remote and viewable management, and also providing a large number of video and image data for deep mining and analysis [8], [9]. The mainly sensors consider the high-definition video camera, and the principle of deployment spacing decided according to its performance and road curve in details.…”