Background subtraction based on phase and distance transform under sudden illumination change
ABSTRACT Effective foreground detection under sudden illumination change is an active research topic. However, most existing background subtraction approaches, which are intensity based, fail to handle this situation. In this paper, we propose a novel background modeling method that overcomes this limitation by relying on statistical models which use pixel phase instead of intensities. We first extract the phase feature of the pixel using Gabor filters. Then, a phase based background subtraction approach is proposed. In this approach, each phase feature is modeled independently by a mixture of Gaussian models and updated with a novel scheme. Since foreground pixels are scattered in the preliminary detection result, distance transform is implemented on the binary image which transforms the image into a distance map. We segment the distance image with a threshold and get the final result. Experiments on two challenging sequences demonstrate the effectiveness and robustness of our method.
- SourceAvailable from: Alexandre Alahi[Show abstract] [Hide abstract]
ABSTRACT: Vision-based background subtraction algorithms model the intensity variation across time to classify a pixel as foreground. Unfortunately, such algorithms are sensitive to appearance changes of the background such as sudden changes of illumination or when videos are projected in the background. In this work, we propose an algorithm to extract foreground silhouettes without modeling the intensity variation across time. Using a camera pair, the stereo mismatch is processed to produce a dense disparity based on a Total Variation (TV) framework. Experimental results show that with sudden changes of background appearance, our proposed TV disparity-based extraction outperforms intensity-based algorithms and existing stereo-based approaches based on temporal depth variation and stereo mismatch.Image Processing (ICIP), 2012 19th IEEE International Conference on; 01/2012
Conference Paper: Spatially adaptive illumination modeling for background subtraction.[Show abstract] [Hide abstract]
ABSTRACT: Background subtraction is important for many vision applications. Existing techniques can adapt to gradual changes in illumination but fail to cope with sudden changes often seen in indoor environment. In this paper, we propose a novel background subtraction technique that models the change of illumination as a regression function of spatial image coordinates. Such spatial dependency is significant when light sources are close to or within the scene. The regression function is learned from highly probable background regions and applied to the rest of the background models to compensate for the illumination change. While a single regression function is adequate for a smooth Lambertian surface, multiple regression functions are needed to handle depth discontinuities, shadows, and non-Lambertian surfaces. The change of illumination is first segmented and different regression functions are applied to different segments. Experimental results comparing our techniques with other schemes show better foreground segmentation during illumination change.IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011; 01/2011
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ABSTRACT: Background subtraction is very important part of surveillance applications for successful segmentation of objects from video sequences. The robust initial background extraction is crucial in any background subtraction. In this paper, we propose an algorithm to extract initial background from surveillance videos using dual frame differences and morphological processing. With the proposed algorithm, the initial background can be extracted accurately and quickly. Experimental results for various environmental sequences are provided to demonstrate the robustness, accuracy and effectiveness of our method.