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Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1061–1068 | Cite as

Smoky vehicle detection based on multi-scale block Tamura features

  • Huanjie Tao
  • Xiaobo Lu
Original Paper

Abstract

Smoky vehicle, emitting visible black exhaust emissions from vehicle exhaust pipe, is representative heavy pollution vehicle. This paper presents an intelligent smoky vehicle detection method based on multi-scale block Tamura features. In this method, the Vibe background subtraction algorithm is adopted to detect vehicle objects. We propose the multi-scale block Tamura features and use this features to distinguish smoky vehicle images and non-smoke vehicle images. More specifically, the region at the back of the vehicle is divided into 1\(\times \)2 blocks. For each block, the multi-scale strategy based on Gaussian kernel with different standard deviations is proposed to extract features and utilize different scales information. Finally, the back-propagation neural network classifier is trained and used for classification. Our method can automatically detect smoky vehicle through analyzing road surveillance videos. The experimental results show that the proposed algorithm framework performs better than common smoke and fire detection method, and the proposed multi-scale block Tamura features can obtain higher detection accuracy than common Tamura features.

Keywords

Smoky vehicle detection Multi-scale block Tamura features Background subtraction Back-propagation neural network 

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AutomationSoutheast UniversityNanjingChina
  2. 2.Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of EducationSoutheast UniversityNanjingChina

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