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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.
Highlights
In the past decades, surveillance cameras have spread across the traffic system for traffic control [1], [2], safe driving and abnormal detection [3], [4]
For MOG2, FPS will be listed in the experiment
As for the H-SqueezeNet model, accuracy, model size, precision P, recall R, and F-measures were utilized as evaluation protocols
Summary
Surveillance cameras have spread across the traffic system for traffic control [1], [2], safe driving and abnormal detection [3], [4]. Vehicle detection application is more and more important for smart cities [5], [6]. The first task of intelligent traffic surveillance is accurate vehicle detection [6]. It is the key technique in the most of traffic applications, such as road real-time monitoring, intelligent tracking and intelligent traffic control [1], [5]. The goal of our system is to achieve vehicle detection and category identification, while meeting real-time requirements
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