Patchwise dictionary learning for video forest fire smoke detection in wavelet domain

• Published in: Neural computing & applications Vol. 33; no. 13; pp. 7965 - 7977
• Main Authors: Wu, Xuehui, Cao, Yichao, Lu, Xiaobo, Leung, Henry
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Decomposition; Dictionaries; Feature extraction; Fog; Forest fires; Haze; Image Processing and Computer Vision; Learning; Original Article; Pixels; Probability and Statistics in Computer Science; Shaking; Smoke; Wavelet transforms; Wavelet; Patchwise dictionary learning; Sparse representation; Forest fire smoke detection
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-05541-y

Fire smoke detection in forest faces much more challenges than in local areas or indoors. Conventional fire smoke methods using static features extracted form single image directly cannot handle some disturbed factors exist in forest, such as shaking trees and fog/haze. On the other hand, it is difficult to capture the dynamic features of fire smoke while they are vital for fire smoke detection. This paper proposes an effective method of extracting features based on sequential images for smoke detection, quasi-dynamic features are extracted using on pixel-block arranged image sets. First, consecutive frames are rearranged into a new big image based on a pixel-block rule, so that the new image still keep the whole structure of original image on block level, and the change of different pixels in the same block, which comes from the same position of each frame, can reflect the dynamic features by special process. Second, to process the new arranged big image, driven by single image wavelet transform for denoting local signal characteristics, Dual Tree-Complex Wavelet Transform is utilized to decompose the new big image into eight images with the same size of the source image after several level-decomposition, including four directions (HH, HL, LH, LL) with two decomposed images in each direction, so that the changes from smoke in different orientation can be extracted and depicted in these subbands. Third, a multi-scale dictionary learning method is proposed to learn dictionaries of each group images. Furthermore, elastic net is introduced for sparse coding and feature coefficients extraction. All operations above contribute to a significant improvement for smoke detection in this paper. Extensive experiments are performed to validate the effectiveness of the proposed approach.