Video flame recognition based on α-GMM and weight kernel sparse representation

Aiming at the problems of incomplete extraction on candidate flame region, a video flame recognition method is proposed based on Gaussian mixture model with adaptive learning rate α (α-GMM) and weight kernel sparse representation. Firstly, a Gaussian mixture model (GMM) for foreground extraction is constructed. Its learning rate α is adjusted adaptively according to the complexity of background change, and candidate flame region is extracted completely by combining the color features in the HSI space. Secondly, dynamic and static features of the flame are extracted from the candidate region, and a feature dictionary is constructed. Finally, a weighted kernel sparse representation classification model based on Mahalanobis distance (MD) is established to implement flame recognition. The kernel function is adopted to solve the nonlinear distribution problem effectively. In order to strengthen the discrimination between classes and improve the flame recognition rate, MD is employed to measure the similarity information between data and construct the weight matrix. The experiment results show that the candidate flame region obtained by the proposed α-GMM is more complete, and the segmentation accuracy is higher. Compared with other classifiers, the accuracy of the proposed weight kernel sparse representation classifier is improved by 10.94% on average, and the false positive rate is reduced by 55.19% on average, which indicates that the proposed method has a higher recognition rate and strong robustness.