Automatic smoky vehicle detection from traffic surveillance video based on vehicle rear detection and multi-feature fusion

摘要

Smoky vehicle emissions remain a significant contributor in many areas where air quality standards are under threat. The existing smoky vehicle detection methods are inefficiency and with high false alarm rate. This study presents an automatic detection method of smoky vehicles from traffic surveillance video based on vehicle rear detection and multi-feature fusion. In this method, the Vibe background subtraction algorithm is utilised to detect foreground objects, and some rules are used to remove non-vehicle objects. To obtain the key region behind the vehicle rear where the most possible has black smoke in, an improved integral projection method is proposed to detect vehicle rear. To analyse if the key region has black smoke, three groups of representative features are designed and extracted to distinguish smoky vehicles and non-smoke vehicles. More specifically, the features include the artificial features based on deep investigation of smoky vehicles, the statistical features based on grey-level co-occurrence matrix, and the frequency domain features based on discrete wavelet transform (DWT). Finally, support vector machine is used as the classifier for the extracted features. The experimental results show that the proposed method achieves lower false alarm rate than the existing smoke detection methods.