Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

Abstract

Fire hazards occurring recently in the world lead to the need of designing accurate fire detection systems in order to save human lives. The newest innovations continue to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications like the adaboost classifier which is well known for its strength in rigid objects detection from images. This paper presents a Fire Detection System (FDS) with an algorithm that works side by side with the adaboost classifier to determine the presence of fire in an image taken by a normal web camera (webcam), in order to decrease the false alarms in an indoor scene. The images are first preprocessed and their selected discrete cosine coefficients are kept for analysis to get better coefficients that will be fed to a neural network for classification and results are compared to a statistical approach used in combination with binary background mask (BBM) and a wavelet-based model of fire's frequency signature(WMF) to test its accuracy.

A System of Driving Fatigue Detection Based on Machine Vision and Its Application on Smart Device

“…Meanwhile, the processing speed can reach 30 fps on PC and 14 fps on tablet, which meets the requirement of real time. Of course, this system could make further improvement on accuracy and speed of detection by using discrete cosine coefficients [24] and covariance feature [25], respectively. In addition, this paper has dodged the conditions under poor illumination.…”