Deep Convolutional Neural Networks based on Manifold for Smoke Recognition

Smoke recognition has been actively studied in the computer vision domain. Due to large variance of smoke color, texture and shapes, smoke recognition is a challenging task. Traditional smoke recognition methods are based on hand-crafted features. In the past few years, some methods which are based on convolutional neural networks have been proposed that achieved great improvement of smoke recognition. However, previous methods cannot capture the internal structure of smoke well. Manifolds can represent the internal characteristics of smoke data in lower dimensions, reduce the redundancy of data representation, and obtain more discriminative capabilities. In this paper, an end-to-end deep convolutional neural network which based on manifold structure was proposed to capture more discriminative features for smoke recognition. Experimental results show that the proposed method achieves promising results on the public smoke recognition dataset. The proposed method can obtain high detection rate, high accuracy rate and low false alarm rate in the same time.