Video synchronization as one-class learning
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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This paper presents a novel disparity map refinement method and vision based surveillance framework for the task of detecting objects of interest in dynamic outdoor environments from two stereo video sequences taken at different times and from different viewing angles by a mobile camera platform. The proposed framework includes several steps, the first of which computes disparity maps of the same scene in two video sequences. Preliminary disparity images are refined based on estimated disparities in neighboring frames. Segmentation is performed to estimate ground planes, which in turn are used for establishing spatial registration between the two video sequences. Finally, the regions of change are detected using the combination of texture and intensity gradient features. We present experiments on detection of objects of different sizes and textures in real videos.