Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods

Abstract

Intelligent transportation system (ITS) collects numerous data for analysis of the transportation system. The data can be used for providing services for travellers and traffic controllers in the ITS and optimizing it, for the purpose of making the transportation more efficient and safer. Due to the wide and flexible employment of video cameras in visual surveillance system (VSS), mature edge-cloud resource scheduling for data transmission and analysis, and the fast development of deep learning, computer vision (CV) methods have been employed in the visual-based ITS services successfully. In this paper, we discuss the edge-cloud surveillance resource scheduling for the CV methods and review the deep learning-based CV methods in the VSS, including detection, classification, and tracking methods, for better understanding of the relationship between the CV-based ITS services and these methods. We experimentally compare several state-of-the-art deep learning-based methods, which have been successfully applied in the CV fields under the ITS scenario, on their performance, inference speed, computational quantity, and model size. According to the comparisons, we propose four main challenges of the deep learning-based CV methods applied in the services, as a discussion of the future research directions. Code are available at https://github.com/PRIS-CV/DL-CV-ITS .