Data Separation of L1-minimization for Real-time Motion Detection

The `1-minimization used to seek the sparse solution restricts the applicability of compressed sensing. This paper proposes a data separation algorithm with computationally efficient strategies to achieve real-time performance of sparse model based motion detection. We use the traditional pursuit algorithms as a pre-process step that converts the iterative optimization into linear addition and multiplication operations. A novel motion detection method is implemented to compare the difference between the current frame and the background model in terms of sparse coefficients. The influence of dynamic texture or statistical noise diminishes after the process of sparse projection; thus, enhancing the robustness of the implementation. Results of the qualitative and quantitative evaluations demonstrate the higher efficiency and effectiveness of the proposed approach compared with those of other competing methods.

[1]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[2]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[3]  Xiaowei Zhou,et al.  Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Nikos Paragios,et al.  Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Yu Liu,et al.  A Noisy Videos Background Subtraction Algorithm Based on Dictionary Learning , 2014, KSII Trans. Internet Inf. Syst..

[7]  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).

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

[9]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[10]  T. Blumensath,et al.  Theory and Applications , 2011 .

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

[12]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[13]  Junzhou Huang,et al.  Background Subtraction Using Low Rank and Group Sparsity Constraints , 2012, ECCV.

[14]  Yu Liu,et al.  A robust motion detection algorithm on noisy videos , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Volkan Cevher,et al.  Compressive Sensing for Background Subtraction , 2008, ECCV.

[16]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

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

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

[19]  Xiaogang Wang,et al.  Background Subtraction via Robust Dictionary Learning , 2011, EURASIP J. Image Video Process..

[20]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[21]  Brian C. Lovell,et al.  Improved Foreground Detection via Block-Based Classifier Cascade With Probabilistic Decision Integration , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  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.

[23]  Junzhou Huang,et al.  Learning with dynamic group sparsity , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[24]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[25]  Xin Liu,et al.  Background subtraction based on low-rank and structured sparse decomposition. , 2015, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.