MUTUAL COMPARATIVE FILTERING FOR CHANGE DETECTION IN VIDEOS WITH UNSTABLE ILLUMINATION CONDITIONS

Abstract. In this paper we propose a new approach for change detection and moving objects detection in videos with unstable, abrupt illumination changes. This approach is based on mutual comparative filters and background normalization. We give the definitions of mutual comparative filters and outline their strong advantage for change detection purposes. Presented approach allows us to deal with changing illumination conditions in a simple and efficient way and does not have drawbacks, which exist in models that assume different color transformation laws. The proposed procedure can be used to improve a number of background modelling methods, which are not specifically designed to work under illumination changes.

[1]  S. Bianco,et al.  How Far Can You Get By Combining Change Detection Algorithms? , 2015, ICIAP.

[2]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[3]  Boris V. Vishnyakov,et al.  DIFFUSION BACKGROUND MODEL FOR MOVING OBJECTS DETECTION , 2015 .

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  F. Xavier Roca,et al.  Exploiting multiple cues in motion segmentation based on background subtraction , 2013, Neurocomputing.

[6]  Max Lu,et al.  Robust and efficient foreground analysis for real-time video surveillance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  D. Scott Wills,et al.  BigBackground-Based Illumination Compensation for Surveillance Video , 2011, EURASIP J. Image Video Process..

[8]  Yang Jie,et al.  Removal of Disturbance of Sudden Illumination Change Based on Color Gradient Fusion Gaussian Model , 2013 .

[9]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[10]  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.

[11]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[12]  Thierry Bouwmans,et al.  Background subtraction via incremental maximum margin criterion: a discriminative subspace approach , 2012, Machine Vision and Applications.

[13]  Frans W Cornelissen,et al.  Local and relational judgements of surface colour: constancy indices and discrimination performance. , 2007, Spatial vision.

[14]  Fatih Murat Porikli,et al.  CDnet 2014: An Expanded Change Detection Benchmark Dataset , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[15]  S. Yu. Zheltov,et al.  SHAPE-BASED IMAGE MATCHING USING HEAT KERNELS AND DIFFUSION MAPS , 2014 .

[16]  Bala Venkatesh,et al.  Morphological image analysis of transmission systems , 2005 .