Smoke vehicle detection based on robust codebook model and robust volume local binary count patterns

Abstract

Smoke vehicle detection is still vulnerable to false alarms. To reduce false alarm rates, this paper presents a smoke vehicle detection method based on Robust Codebook (R-Codebook) model and Robust Volume Local Binary Count (R-VLBC) pattern. In detecting suspected smoke blocks, we propose the R-Codebook model, which is robust to illumination changes and camera shaking or leaf shaking in the scene. In recognizing smoke block sequences, firstly, we propose a robust and discriminative descriptor called Non-Redundant VLBC (NR-VLBC). The feature dimension is reduced by half than the VLBC to overcome the issue that the VLBC is sensitive to the relative changes between background and foreground. Secondly, we propose a new Completed VLBC (CVLBC) by combining VLBC-Sign, VLBC-Magnitude, VLBC-Center gray level and VLBC-Difference to characterize spatial–temporal features. Thirdly, two strategies are used to further improve the proposed CVLBC to form R-VLBC. One is to overcome the issue that the CVLBC is sensitive to noise based on using a weighted local threshold (WLT), which is robust to noise and illumination variants and also make a balance between noise resistance and information of individual pixel. The other one is to extract multi-scale information by using a set of radius and sampling points. Extensive experiments show that the proposed method can achieve better performances than existing methods.