Spatial Pyramid Pooling in Structured Sparse Representation for Flame Detection

Abstract

Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents a novel solid solution based on sparse representation with spatial pyramid pooling. Traditional sparse representation, as one of prevalent feature learning methods, is successfully applied for object detection. But it has some intrinsic defects. Firstly, it requires fixed input image size. Secondly, the accuracy of detection heavily depends on discriminative dictionary learning and feature coding. At last, it is usually very time-consuming. In this paper, we have proposed a novel dictionary learning method with the structure sparsity constraint to train a discriminative dictionary. In feature coding stage, we compute sparse codes of each patch with dictionaries learned from data and pool them to form local histogram in spatial pyramid manner. At lat, the feature vector is pipelined into a linear SVM classifier to train the model. For improving the efficiency, we also adopt the selective search approach to generate the candidate region proposals in the preprocessing stage. In processing test images, our method achieved better or comparable accuracy to the state-of-the-art on FlameDetection2010 Dataset.