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Smoky vehicle detection in surveillance video based on gray level co-occurrence matrix
Author(s): Huanjie Tao; Xiaobo Lu
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Paper 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.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080642 (9 August 2018); doi: 10.1117/12.2502873
Show Author Affiliations
Huanjie Tao, Southeast Univ. (China)
Xiaobo Lu, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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