Studies On Fire Detection Based On Video-image Processing For Large Space Structures

Video-image fire detection performs well in detection range, response time and cost. So it is more efficient than traditional methods in fire detection for large space structures. In essence, video-image fire detection is composed of three parts: object extraction, tracking and recognition. Due to the complexity of the visual environment in the large space structures, such as change of illumination, moving of objects, large number of objects, variation of motion model, object shading, lens shading and the problem of the small sample size, it is hard to realize accurate, real-time and robust detection of fire at the same time. In this research, a series of algorithms about object extraction, tracking and recognition are improved and proposed. A prototype software system is developed to detect fire for large space structures. The main contents are as follows:(1) Object extraction. Due to the changing of lighting and moving of objects, it is difficult to achieve accurate and real-time object extraction. First, the feature and performance of main extraction algorithms are analyzed. Then a new space-time adaptive algorithm for background updating is proposed for the background subtraction. The advance is that the information about object tracking and recognition can be applied to guide the object extraction. So the type, position and frequency of the object can be gained and considered.(2) Object tracking. The key problem is the variation motion model and shading in multi-object tracking. First, a data structure is established for multi-object extraction, tracking and recognition. Then a new concised SR-UKF(square root unscented kalman filter) is proposed when the measuring vector is a subset of the state vector. At last, multiple motion model, data delay and fuzzy adaption are introduced into the multi-object tracking system to improve the tracking performance. So a new multi-object tracking algorithm is proposed. That is, fuzzy adaptive multi-object tracking algorithm based on multiple model and data delay.(3) Object recognition. In order to conquer the problem of the small sample size in fire detection for large space structures, functions of membership degree are constituted based on the final training result of the GA(genetic algorithm) in GALSSVM(genetic algorithm based least square support vector machine). So a new SVM is proposed with the name FGALSSVM(fuzzy membership and genetic algorithm based least square support vector machine). To improve the accuracy and robustness of single and transient recognition algorithms, the concept of transient data fusion and historical data fusion are introduced into the system of recognition. So a new type of recognition frame is established.Except for improving the algorithms, the source codes for main algorithms and the prototype detecting software are developed. A series of tests are conducted to verify the performance of the improved algorithms.