Image based fire detection and alarm technology which makes use of cameras as a probe, the scene of the image information and digital image processing technology, identify fire flame and smoke and then determine whether the fire occurred. Since the technology above can be applied to early warning and outdoor fire detection such as large space, it gets more and more attention in the recent years and good results in particular experimental environment. However, in practical applications, due to fire derivatives variability and complexity of the scene, this technology has a relatively high false alarm rate and does not have enough robustness and adaptability. In this paper, a robust and broad applicability flame and smoke detection algorithm is designed by analyzing image information of the fire development from which broke through. Finally, an algorithm, combination of temporal and spatial information to assess the fire alarm algorithm is implemented by combining the performance requirements of the fire alarm system. Specifically, as the paper work development processed, the major research results and work have been done as follow: (1) Carry out a detailed analysis of the fire flames and smoke image information, summary most of the possible major interference sources and do a classification. According to the characteristics of various types of interference sources in combination with fire flames and smoke image information, extracts effective visual features to eliminate interference and designs the whole algorithms. (2) A fire flame detection algorithm is presented, which based on the HSV color segmentation of the flame region combined with the fire flame closed angle character. The Experiments results show that the algorithm has low time and space complexity, and can achieve real-time fire flame detection. (3) A Multi-source fire smoke detection algorithm is presented. This algorithm utilizes an improved Gaussian mixture model positioning, target following as well as consistency in the brightness, motion accumulation and spread, these three effective static and dynamic characteristics. The Experimental results show that the algorithm is able to rule out the major interference source. For 320 240 size video image frame, the processing time for each frame is about 40ms which meets the accuracy and speed needs of practical engineering requirement. (4) A few key issues about system design are proposed in light of the actual performance requirements. Thro...
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