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: Thierry Bouwmans[Show abstract] [Hide abstract]
ABSTRACT: Background modeling is currently used to detect moving objects in video acquired from static cameras. Numerous statistical methods have been developed over the recent years. The aim of this paper is firstly to provide an extended and updated survey of the recent researches and patents which concern statistical background modeling and secondly to achieve a comparative evaluation. For this, we firstly classified the statistical methods in term of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in term of strategies used. Furthermore, we discussed them in term of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.Recent Patents on Computer Science. 01/2011; 4:147-176.
- [Show abstract] [Hide abstract]
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.01/2011;
- [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