Vehicles Detection System at Different Weather Conditions

Abstract

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number of pre-processing operations, such as Gaussian blur filter, dilation, erosion, and threshold. The main contribution of this paper is to propose a histogram equalization technique for complex weather conditions instead of a Gaussian blur filter for improving the video clarity, which leads to increase detection accuracy. Based on the previous steps, the system defines each region in the frame expected to contain vehicles. Finally, Support Vector Machine (SVM) classifies the defined regions into a vehicle or not.  As compared to the previous methods, the proposed system showed high efficiency of about 96.4% and ability to detect vehicles at different weather conditions.