Background Subtraction For Visual Surveillance: A Fuzzy Approach
Developing a background subtraction method, researchers must design each step and choose the features in relation to the critical situations they want to handle. All these critical situations generates imprecision and uncertainties in all the process of background subtraction. Therefore, some authors have recently introduced fuzzy concepts in the different steps of background subtraction as follows:
T. Bouwmans, “Background Subtraction For Visual Surveillance: A Fuzzy Approach”, Handbook on Soft Computing for Video Surveillance, Taylor and Francis Group, Chapter 5, March 2012.
Fuzzy Background Modeling
Gaussian Mixture Models (GMMs) are the most popular techniques in background modeling but present some limitations when some dynamic changes occur like camera jitter, illumination changes, movement in the background. Furthermore, the GMM are initialized using a training sequence which may be noisy and/or insufficient to model correctly the background. All these critical situations generate false classification in the foreground detection mask due to the related uncertainty. In this context, we propose to model the background by using a Type-2 Fuzzy Gaussian Mixture Models. The interest is to introduce descriptions of uncertain parameters in the GMM. Experimental validation of the proposed method is performed and presented on a diverse set of RGB and infrared videos. Results show the relevance of the proposed approach. (more information)
F. El Baf, T. Bouwmans, B. Vachon, “Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling”, International Symposium on Visual Computing, ISVC 2008, pages 772-781, Las Vegas, USA, December 2008.
F. El Baf, T. Bouwmans, B. Vachon, “Fuzzy Statistical Modeling of Dynamic Backgrounds for Moving Object Detection in Infrared Videos”, OTCBVS 2009, pages 60-65, Miami, Florida, June 2009
T. Bouwmans, F. El Baf, “Modeling of Dynamic Backgrounds by Type-2 Fuzzy Gaussians Mixture Models”, MASAUM Journal of Basic and Applied Sciences, Volume 1, Issue 2, pages 265-277, September 2009.
Fuzzy Background Maintenance
The background maintenance determines how the background will adapt itself to take into account the critical situations which can occurred. In the literature, there are two maintenance schemes: the blind one and the selective one. The blind background maintenance consists to update all the pixels with the same rules. The disadvantage of this scheme is that the value of pixels classified as foreground are taken into account in the computation of the new background and so polluted the background image. To solve this problem, some authors use a selective maintenance which consists of computing the new background image with a different learning rate following its previous classification into foreground or background The disadvantage of the selective maintenance is mainly due to the crisp decision which attributes a different rule following the classification in background or foreground. To solve this problem, we propose to take into account the uncertainty of the classification. (more information)
F. El Baf, T. Bouwmans, B. Vachon, “A Fuzzy Approach for Background Subtraction”, IEEE International Conference on Image Processing, ICIP 2008, pages 2648-2651, San Diego, California, U.S.A, October 2008.
Fuzzy Foreground Detection
Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations can occur in the scene and generate a false classification of image pixels. To deal with the uncertainty in the classification issue, we propose to use the Choquet integral as aggregation operator. Experiments on different data sets in video surveillance have shown a robustness of the proposed method against some critical situations when fusing color and texture features. Different color spaces have been tested to improve the insensitivity of the detection to the illumination changes. Then, the algorithm has been compared with another fuzzy approach based on the Sugeno integral and has proved its robustness (more information)
F. El Baf, T. Bouwmans, B. Vachon, “Foreground Detection using the Choquet Integral”, International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2008, pages 187-190, Klagenfurt, Austria, May 2008.
F. El Baf, T. Bouwmans, B. Vachon, “Fuzzy Foreground Detection for Infrared Videos”, Joint IEEE International Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum, OTCBVS 2008, pages 1-6, Anchorage, Alaska, USA, 27 June 2008.
F. El Baf, T. Bouwmans, B. Vachon, “Fuzzy Integral for Moving Object Detection”, IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2008, pages 1729-1736, Hong-Kong, China, 1-6 June 2008.