Smoky vehicle detection in surveillance video based on gray level co-occurrence matrix

Abstract

The vehicle with harmful black smoke pollutant emitted from vehicle exhaust pipe is usually called smoky vehicle. Existing smoky vehicle detection methods mainly lie on traditional manual monitoring. In this paper, we propose an intelligent smoky vehicle detection method based on Gray Level Co-occurrence Matrix (GLCM). This method can automatically detect smoky vehicles through analyzing the road surveillance videos. More specifically, we adopt Vibe background subtraction algorithm to detect vehicle objects. The gray-level integral projection technology and image local range technology are combined to detect the vehicle rear. We extract GLCM from the region at the back of the vehicle, and five different GLCM-based features, namely, angular second moment (ASM), entropy (ENT), contrast (CON), correlation (COR), and inverse difference moment (IDM), are selected to distinguish smoky images and nonsmoke images. The back propagation (BP) neural network is adopted to train the classifier and classify new samples. The experimental results show that the proposed method has a good performance.