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

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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.