Fuzzy foreground detection for infrared videos

We present a foreground detection algorithm based on a fuzzy integral that is particularly suitable for infrared videos. The proposed detection of moving objects is based on fusing intensity and textures using fuzzy integral. The detection results are then used to update the background in a fuzzy way. This method allows to robustly detect moving object in presence of cloudy and rainy conditions. Our theoretical and experimental results show that the proposed method gives similar results than the KaewTraKulPong and Bowden approach based on Mixture Of Gaussians (MOG) with less memory requirement and time consuming. The results using the OTCBVS benchmark/test dataset videos show the robustness of the proposed method.

[1]  Michio Sugeno,et al.  A new approach to time series modeling with fuzzy measures and the Choquet integral , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[2]  Vasile Gui,et al.  A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation , 2005 .

[3]  Dragoljub Pokrajac,et al.  Tracking motion objects in infrared videos , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[4]  Mohan M. Trivedi,et al.  "Hybrid Cone-Cylinder" Codebook Model for Foreground Detection with Shadow and Highlight Suppression , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[5]  Oncel Tuzel,et al.  Bayesian background modeling for foreground detection , 2005, VSSN@MM.

[6]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[7]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[8]  Riad I. Hammoud,et al.  Robust Multi-Pedestrian Tracking in Thermal-Visible Surveillance Videos , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[9]  Yasuo Narukawa,et al.  Decision Modelling Using the Choquet Integral , 2004, MDAI.

[10]  Richard Bowden,et al.  Adaptive Visual System for Tracking Low Resolution Colour Targets , 2001, BMVC.

[11]  Nizar Bouguila,et al.  A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[12]  James M. Keller,et al.  Information fusion in computer vision using the fuzzy integral , 1990, IEEE Trans. Syst. Man Cybern..

[13]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[14]  Bir Bhanu,et al.  Physics-based models of color and IR video for sensor fusion , 2003, Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI2003..

[15]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Nigel J. B. McFarlane,et al.  Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.

[17]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  M.M. Trivedi,et al.  Vision modules for a multi-sensory bridge monitoring approach , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[20]  Dima Damen,et al.  Detecting Carried Objects in Short Video Sequences , 2008, ECCV.

[21]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[22]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[23]  James W. Davis,et al.  Background-Subtraction in Thermal Imagery Using Contour Saliency , 2007, International Journal of Computer Vision.

[24]  Alexandre R. J. François,et al.  Adaptive Color Background Modeling for Real-Time Segmentation of Video Streams* , 1999 .

[25]  Mark E Hallenbeck,et al.  Extracting Roadway Background Image , 2006 .

[26]  Hong Huo,et al.  Traffic Video Segmentation Using Adaptive-K Gaussian Mixture Model , 2006, IWICPAS.

[27]  Monica N. Nicolescu,et al.  Improving target detection by coupling it with tracking , 2009, Machine Vision and Applications.

[28]  Ming Zhao,et al.  ROBUST AUTOMATIC VIDEO OBJEDT SEGMENTATION TECHNIQUE , 2003 .

[29]  Hongxun zhang,et al.  Fusing Color and Texture Features for Background Model , 2006, FSKD.