Effective Scene Matching For Intelligent Video Surveillance

This paper proposes a novel method on scene matching which aims to detect the unauthorized change of the camera's field of view (FOV) automatically. The problem is substantially difficult due to mixed representation of FOV change and scene content variation in actual situation. In this work, a local viewpoint-invariant descriptor is firstly proposed to measure the appearance similarity of the captured scenes. And then the structural similarity constraint is adopted to further distinguish whether the current scene remains despite the content change in the scene. Experimental results demonstrate that the proposed method works well in existence of viewpoint change, partial occlusion and structural similarities in real environment. The proposed scheme has been proved to be practically applicable and reliable by its use in an actual intelligent surveillance system.