Abstract
This study provides an overview of the sensor technologies commonly used for automated vehicle classification and counting, with a focus on non-intrusive sensors. Video cameras are found to be the most feasible solution for data collection in traffic census as it can operate in portable mode and used at any location. Several factors must be considered to ensure accurate counting. These involve optimum placement of the camera to ensure that all vehicles can be observed, and the lighting conditions must be considered to ensure good video quality. These further contributes to accurate classification and counting of vehicles by dedicated deep learning algorithm. As the data collection may involve location with poor access to cloud computing and storage, offline processing is therefore recommended. The study also revealed opportunities for solving issues related to strategic placement of video cameras, and development of dedicated deep learning algorithms.
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