A Robust Multiple Moving Vehicle Tracking for Intelligent Transportation System

The proposed framework accurately counts and classifies the vehicle color of detected vehicles for a real-time video input. The proposed methodology maps the vehicle features for detecting the moving vehicles using background subtraction using KNN, a binary mask, and morphological operations such as erosion and dilation which efficiently eliminate dynamic shadow and extract the vehicle region. Further, unique Id is been assigned for detected vehicles to avoid repetitive counting of detected vehicles when it passes through the counting zone. Furthermore, the colors of the vehicles are been identified by estimating similarity measure between trained and tested using k-Nearest Neighbors machine learning classifier. The RGB intensity with a maximum frequency of occurrence is reflected as the color of vehicle. Experimental results show that the proposed system outperforms compared to other state-of-the-art methods such as mixture of Gaussian2 (MOG2) and Godbehere–Matsukawa–Goldberg (GMG) resulted in false detection due to its inefficiency in shadow removal resulting in an overall efficiency of 94% consuming less computational time for yielding the output.