Vision-based traffic surveillance systems are amidst the most reliable, inexpensive and highly applicable methodologies for surveying traffic conditions. The implementation of these strategies, however, is limited under certain conditions, such as the presence of vehicle occlusions or poor illumination conditions that lead to either over-counted or undercounted traffic data. The proposed motion-based methodology aims at overcoming these limitations by employing a new technique for full-body occlusion handling of vehicle cars. The methodology is based on five main steps and three main methods: Background Subtraction, Histogram of Oriented Gradients (HOG) trained by linear Support Vector Machine (SVM), Haar-Like features (HL) trained by Adaboost and Vehicle Counting. The proposed methodology is tested with various 30-minute videos and 452 pre-identified cases of occlusion. Preliminary results indicate that the proposed methodology is reliable and robust in providing traffic density analysis. Future work may rely on the extension of the proposed methodology to deal with the detection of vehicles moving towards multiple directions
