Byoungmoo Lee

Real-time fire detection using camera sequence image in tunnel environment

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. However it is impossible to keep the human observation of Closed-Circuit Television (CCTV) in tunnel for 24 hour. So if the fire and smoke detection system through image processing can warn fire state, it will be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm

Development of early tunnel fire detection algorithm using the image processing

To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident fast. However it is impossible to keep the human observation of CCTV in tunnel for 24 hour. So if the fire and smoke detection system through image processing warn fire state, it can be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. In this paper, we proposed algorithm using the image processing, which is an early detection of the fire and smoke occurrence in the tunnel. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stay of detection. In addition, by comparing properties of each algorithm throughout experiment, we have proved the propriety of algorithm.

Real-time object segmentation using disparity map of stereo matching

This paper presents algorithms for the real-time object segmentation of the noisy disparity map obtained by stereo matching algorithm and its verification test using hardware architectures. The disparity map contains lots of noise from various causes, and it has to be refined by some noise filtering methods to make it useful for the object segmentation. Therefore refinement process is a necessary process prior to segmentation process. In our approach, refinement method based on noise removal technique is adopted for improvement of the disparity map quality. And the projection-based region merging method is used for object segmentation. The proposed algorithms are implemented in FPGA board. Results of the test show that our approach works precisely and its performance fits in conditions of real-time application. The developed real-time object segmentation system could be useful for various applications such as face recognition, object tracking, and other applications with the support of proper embedded software.

REAL-TIME FIRE AND SMOKE DETECTION USING CAMERA SEQUENCE IMAGE IN TUNNEL ENVIRONMENT

In this paper, we proposed an image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurring in the tunnel, it is necessary to have a system to sense and minimize the incident as fast as possible. However it is impossible for human observation of Closed-Circuit Television (CCTV) in tunnel for 24 h. So if the fire and smoke detection system through image processing can warn a fire, it will be very convenient, and it can be possible to minimize damage even when no one is in front of the monitor. The fire and smoke detection is different from forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so an indigenous algorithm has to be developed. The two algorithms proposed in this paper are able to detect the exact position at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.