Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems (ITS) in vehicle tracking application. The shadow is the source of error for vehicle detection, which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting, vehicle detection, vehicle tracking, and classification. Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets, but the process of extracting shadows from moving vehicles in low light of real scenes is difficult. The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem. This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking. The method is distributed into two phases: In the first phase, we extract foreground regions using a mixture of Gaussian model, and then in the second phase, with the help of the Gamma correction, intensity ratio, negative transformation, and a combination of Gaussian filters, we locate and remove the shadow region from the foreground areas. Compared to the outcomes proposed method with outcomes of an existing method, the suggested method achieves an average true negative rate of above 90%, a shadow detection rate SDR (η%), and a shadow discrimination rate SDR (ξ%) of 80%. Hence, the suggested method is more appropriate for moving shadow detection in real scenes.