Vehicle classification and counting system

Vehicle classification and counting play an important role in the intelligent transportation system, as they may serve to improve traffic congestion and safety problems. Therefore, this study has developed a real-time and vision-based vehicle classification and counting system. This will involve establishing Time-Spatial Images (TSI) from input video, removing the shadow portions in TSI through the use of Support Vector Machine (SVM) and Deterministic Non-Model Based Approach, detecting the Region of Interest (ROI) through a simple morphology process, and finally using the ROI accumulative curve method and Fuzzy Constraints Satisfaction Propagation (FCSP) to process occlusion problems and perform vehicle classification and counting. The experimental results have shown that the proposed method is feasible.