Adaptive flame detection using randomness testing and robust features

Abstract This paper presents a novel approach to detect flame based on robust features and randomness testing. The flame color probability is estimated based on a Gaussian model learned in the YCbCr color space. The motion probability is then obtained by employing the background image updated dynamically with an approximate median method. The color and motion probabilities are integrated in order to obtain flame candidates, from which a feature vector comprised of seven features is extracted for each frame. The successive feature vectors are then applied to the Wald–Wolfowitz randomness test in order to obtain the prior flame probability. Finally, the convolution is defined in order to update the prior probability into a posterior probability for improving the system reliability, and an alarm level is determined based on the posterior probability. The presented method was successfully applied to real-environment intelligent surveillance systems and proved to be effective, robust, and adaptive, irrespective of the environment, weather conditions, or video quality.