Abstract Recent advances in video processing technologies have led to a wave of research on computer vision-based fire detection systems. This paper presents a multi-level framework for fire detection that analyses patterns in chromatic information, shape transmutation, and optical flow estimation of fire. First, the decision function of fire pixels based on chromatic information uses majority voting among state-of-the-art fire color detection rules to extract the regions of interest. The extracted pixels are then verified for authenticity by examining the dynamics of shape. Finally, a measure of turbulence is assessed by an enhanced optical flow analysis algorithm to confirm the presence of fire. To evaluate the performance of the proposed model, we utilize videos from the Mivia and Zenodo datasets, which have a diverse set of scenarios including indoor, outdoor, and forest fires, along with videos containing no fire. The proposed model exhibits an average accuracy of 97.2% for our tested dataset. In addition, the experimental results demonstrate that the proposed model significantly reduces the rate of false alarms compared to the other existing models.

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[17] Shon D., Kang M., Seo J., and MyonKim J., Frontier and Innovation in Future Computing and Communications, Springer, 2014. Computer Vision-based Early Fire Detection Using Enhanced Chromatic ... 953 Arnisha Khondaker completed B.Sc. Degree in Computer Science and Engineering (CSE) from Brac University (BracU), Bangladesh in 2018. Currently, she is a Lecturer in the CSE department at BracU. Her research interests are computer vision and artificial intelligence. Arman Khandaker completed his B.Sc. Degree in CSE from BracU, Bangladeshin 2018. Currently he is working as a software engineer. His research interests include fire detection and computer vision. Jia Uddin received a BSc degree in Computer and Communication Engineering from International Islamic University Chittagong, Bangladesh in 2005, and an MSc. degree in Telecommunications from the Blekinge Institute of Technology, Sweden, in 2010. Hedid Ph.D. in Computer Engineering from the University of Ulsan, Korea, in January 2015. He is an Assistant Professor in Department of Technology Studies, Endicott College, Woosong University, South Korea and an Associate Professor (On Leave), Computer Science and Engineering Department at BracU, Bangladesh. His research interests include fault diagnosis, computer vision, and multimedia signal processing.