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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.

A Multi-Class Multi-Movement Vehicle Counting Framework for Traffic Analysis in Complex Areas Using CCTV Systems