Adaptive local spatial modeling for online change detection under abrupt dynamic background

Change detection is an important theme in video processing. To provide reliable detection results in challenging scenes, traditional methods introduced sophisticated statistical distributions and handcraft spatial features to build background models. In this paper, we develop an intuitive background model based on simple statistical distribution and adaptive spatial correlation among pixels: For each observed pixel, we select a group of supporting pixels with high correlation, and then employ a single Gaussian to model the intensity deviations of each pixel pair. To compensate camera motion and fast adapt to dynamic pattern that coming afterwards, a randomized multichannel on-line updating mechanism is introduced. This observation is robust to abrupt illumination variation and dynamic background. Experimental results using all the video sequences provided by three challenging benchmarks (CDW-2012, CDW-2014 and SABS) validate it outperforms many state-of-the-art methods under various situations.

[1]  Brian C. Lovell,et al.  Shadow detection: A survey and comparative evaluation of recent methods , 2012, Pattern Recognit..

[2]  David Suter,et al.  Background Subtraction Based on a Robust Consensus Method , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  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.

[4]  Mohammed Bennamoun,et al.  Automatic Shadow Detection and Removal from a Single Image , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Thuong Le-Tien,et al.  NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

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

[7]  Yutaka Satoh,et al.  Object detection based on a robust and accurate statistical multi-point-pair model , 2011, Pattern Recognit..

[8]  Gerhard Rigoll,et al.  Background segmentation with feedback: The Pixel-Based Adaptive Segmenter , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[9]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[11]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[12]  Matti Pietikäinen,et al.  Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Mohan M. Trivedi,et al.  Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Gang Wang,et al.  Spatiotemporal Background Subtraction Using Minimum Spanning Tree and Optical Flow , 2014, ECCV.

[15]  Dong Liang,et al.  Co-occurrence probability-based pixel pairs background model for robust object detection in dynamic scenes , 2015, Pattern Recognit..

[16]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

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

[18]  Allen R. Hanson,et al.  Background modeling using adaptive pixelwise kernel variances in a hybrid feature space , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Thierry Bouwmans,et al.  Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .

[20]  Dong Liang,et al.  Co-occurrence-based adaptive background model for robust object detection , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[21]  I. Haritaoglu,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .

[22]  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.