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Image-based vehicle tracking and classification on the highway

In recent years, the development of automatic traffic surveillance system has received great attention in the academic and industrial research. Based on computer vision technology, the purpose of this work is to construct the traffic surveillance system on the highway for estimating traffic parameters, such as vehicle counting and classification. The proposed system mainly consists of three steps including vehicle region extraction, vehicle tracking, and classification. The background subtraction method is firstly utilized to extract the foreground regions from the highway scene. Some geometric properties are applied to remove the false regions and shadow removal algorithm is used for obtaining more accurate segmentation results. After vehicle detection, a graph-based vehicle tracking method is used for building the correspondence between vehicles detected at different time instants. Finally, we introduce two measures, such as aspect ratio and compactness to classify vehicles. In the experiment, three videos with different lighting conditions are used to demonstrate the effectiveness of our proposed system.