Recursive Bayesian fire recognition using greedy margin-maximizing clustering

Abstract

Vision-based fire detection is a challenging research area, since the visual features of fire dynamically change due to several factors such as weather conditions. In this paper, we propose a novel fire detection approach in which detected fire-candidate blobs are categorized as fire or non-fire under recursive Bayesian estimation. By employing the recursive estimation, we attempt to deal with fire characteristics that are dynamic as well as spatiotemporally continuous in a hidden Markov process. More specifically, for each detected fire-candidate blob, future beliefs about hidden classes are predicted and corrected by the most recent beliefs and observations of the blob. This is repeated during the lifetime of the blob. In this framework, to reduce the Bayes error in classification, we devised the greedy margin-maximizing clustering algorithm. This algorithm learns color clusters to model the feature space while attempting to maximize the in-cluster margins within a class and between classes. To further improve the detection accuracy, we developed two methods, $\epsilon$-time delayed decision and on-line learning of transition probability. These were invented to suppress false alarms caused by temporary fire-like instances and to determine the current class by considering the majority of previous classification results. Experiments and comparative analyses with two contemporary approaches are conducted for various fire situations. The results show that the proposed approach is superior to the previous approaches in detecting fire and reducing false alarms. Furthermore, the proposed approach is shown to be competitive in applications to real environments.