Design And Implementation Of The Video Based Intelligent Warning System Against Nature Disaster For The Qinghai-Tibet Railway

Qinghai-Tibet railway is a fundamental national transportation facility, which plays a very important role either in economic or politics. But the railway lies in the Qinghai-Tibet plateau, where the nature environment is too atrocious to employ human to patrol around. Besides, the railway is easily to be damaged by sand, snow or other disaster. So how to monitor its safety through video cameras and giving out warnings in time is a serious problem.The key point of the problem is how to detect railway disaster correctly and in time under any weather condition (sunny, cloudy, rainy, windy) and any illumination condition (stronglighting, reflectlighting, backlighting).After researching and analyzing current intelligent video surveillance technics, we design and implement a warning system to automatically detect whether the objects'(railway and its base , bridge, etc.) features are lost. In the system, we employ a phase-only correlation method to deal with image shaking, and we also employ gradient projection based algorithm to remove illumination. Then we extract the orientation, edge, intrinsic image as the primary invariable features, based on which we develop a algorithm to calculate the region of interest automatically. After that, we use a multi-template, multi-feature matching algorithm to detect whether the objects' feathers are lost.The contribution of this thesis is:1. Design and implement a background analysis system to automatically detect the disasters for the Qinghai-Tibet railway.2. Develop a orientation based algorithm to detect the region of interest(ROI) automatically.3. Develop a intrinsic image based, multi-template, multi-feature matching algorithm to detect disasters in real time.In order to test the system , we build a modal that simulate the real railway scene, on which we do damage to simulate disasters and run warning and false-warning test. The result of our experiment proves the system works correctly and efficiently as we planned. Right now, our system is running online and works well.