Real-time surveillance video from traffic cameras was taken as an input, and was split into frames. An algorithm was designed to extract HOG and GLCM features from the images and classify using Multi-Class Support Vector Machines and Parallel Algorithms. This trained model was used to detect pedestrian-vehicle interaction.