Abstract
Traffic analysis using computer vision techniques is attracting more attention for the development of intelligent transportation systems. Consequently, counting traffic volume based on the CCTV system is one of the main applications. However, this issue is still a challenging task, especially in the case of complex areas that involve many vehicle movements. This study performs an investigation of how to improve video-based vehicle counting for traffic analysis. Specifically, we propose a comprehensive framework with multiple classes and movements for vehicle counting. In particular, we first adopt state-of-the-art deep learning methods for vehicle detection and tracking. Then, an appropriate trajectory approach for monitoring the movements of vehicles using distinguished regions tracking is presented in order to improve the performance of the counting. Regarding the experiment, we collect and pre-process the CCTV data at a complex intersection to evaluate our proposed framework. In particular, the implementation indicates the promising results of our proposed method, which achieve accuracy around 80% to 98% for different movements for a very complex scenario with only a single view of the camera.
Highlights
Traffic flow analysis is an important fundamental for urban planning and management of the Intelligent Transportation System (ITS)
This study proposes a comprehensive framework with multi-class and multi-movement vehicle counting in which we focus on short-term vehicle tracking based on semantic regions in order to improve the tracking process; the accuracy of counting will be improved
We compared our proposed method with vehicle counting using virtual lines, which is presented in Algorithm 1
Summary
Traffic flow analysis is an important fundamental for urban planning and management of the Intelligent Transportation System (ITS). When the connected environment is still far from reality and developing Wireless Sensor Network (WSN) faces expensive cost and transmission problems, analyzing traffic flow from low-cost video surveillance (CCTV) systems becomes a promising solution [2]. By monitoring traffic flow from CCTV, we able to evaluate and verify the performance of the system. In the case of vehicle monitoring, the video-based system is able to track the different movements of vehicles by a monocular camera instead of developing multiple sensors locating in each direction of the surveillance systems (e.g., loop detectors). Video-based vehicle counting becomes a key technique for traffic analysis in complex areas [4,5]
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