(46-5) 12 * << * >> * Русский * English * Содержание * Все выпуски

Robust hybrid technique for moving object detection and tracking using cartoon features and fast PCP
S.H. Jeevith 1, S. Lakshmikanth 2

1 Sri Siddhartha Institute of Technology, Tumakuru-572105, India;
2 Acharya Institute of Technology, Bengaluru-560107, India

 PDF, 924 kB

DOI: 10.18287/2412-6179-CO-1056

Страницы: 783-789.

Язык статьи: English.

Аннотация:
In various computer vision applications, the moving object detection is an essential step. Principal Component Analysis (PCA) techniques are often used for this purpose. However, the performance of this method is degraded by camera shake, hidden moving objects, dynamic background scenes, and / or fluctuating exposure. Robust Principal Component Analysis (RPCA) is a useful approach for reducing stationary background noise as it can recover low rank matrices. That is, moving object is formed by the low power models and the static background of RPCA. This paper proposes a simple alternative minimization algorithm to fix minor discrepancies in the original Principal Component Pursuit (PCP) or RPCA function. A novel hybrid method of cartoon texture features used as a data matrix for RPCA taking into account low-ranking and rare matrix is presented. A new non-convex function is proposed to better control the low-range properties of the video background. Simulation results demonstrate that the proposed algorithm is capable of giving consistent random estimates and can indeed improve the accuracy of object recognition in comparison with existing methods.

Citation:
Jeevith SH, Lakshmikanth S. Robust hybrid technique for moving object detection and tracking using cartoon features and fast PCP. Computer Optics 2022; 46(5): 783-789. DOI: 10.18287/2412-6179-CO-1056.

References:
  1. Jeevith SH, Lakshmikanth S. Detection and tracking of moving object using modified background subtraction and Kalman filter. Int J Electr Comput Eng 2021; 11(1): 217-223.
  2. Liu K, Zhang J. Moving object detection based on improved ViBe algorithm. Proc SPIE 2021; 11736: 117360N.
  3. Varcheie PDZ, Sills-Lavoie M, Bilodeau GA. A multiscale region-based motion detection and background subtraction algorithm. Sensors 2010; 10(2): 1041-1061.
  4. Varadarajan S, Miller P, Zhou H. Region-based mixture of Gaussians modelling for foreground detection in dynamic scenes. Pattern Recognit 2015; 48(11): 3488-3503.
  5. Candès EJ, Li X, Ma Y, Wright J. Robust principal component analysis? J ACM 2011; 58(3): 11.
  6. Zhou X, Yang C, Yu W. Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell 2012; 35(3): 597-610.
  7. Gao Z, Cheong LF, Wang YX. Block-sparse RPCA for salient motion detection. IEEE Trans Pattern Anal Mach Intell 2014; 36(10): 1975-1987.
  8. Sun Y, Tao X, Li Y, Lu J. Robust 2D principal component analysis: A structured sparsity regularized approach. IEEE Trans Image Process 2015; 24(8): 2515-2526.
  9. Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. Proc IEEE conf on Computer Vision and Pattern Recognition 2014: 580-587.
  10. He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 2015; 37(9): 1904-1916.
  11. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. 2015. arXiv preprint. Source: <https://arxiv.org/abs/1506.01497>.
  12. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proc IEEE conf on computer vision and pattern recognition 2016: 779-788.
  13. Welcome to the PROPACK homepage. Source: <http://sun.stanford.edu/~rmunk/PROPACK>.
  14. Rodriguez P, Wohlberg B. Incremental principal component pursuit for video background modeling. J Math Imaging Vis 2016; 55(1): 1-18.
  15. Lo BPL, Velastin SA. Automatic congestion detection system for underground platforms. Proc 2001 Int Symp on Intelligent Multimedia, Video and Speech Processing (ISIMP 2001) (IEEE Cat. No. 01EX489) 2001; 158-161.
  16. Osher S, Solé A, Vese L. Image decomposition and restoration using total variation minimization and the H1. Multiscale Model Simul 2003; 1(3): 349-370.
  17. Buades A, Le TM, Morel JM, Vese LA. Fast cartoon+ texture image filters. IEEE Trans Image Process 2010; 19(8): 1978-1986.
  18. Giraldo-Zuluaga JH, Salazar A, Gomez A, Diaz-Pulido A. Camera-trap images segmentation using multi-layer robust principal component analysis. The Visual Computer 2019; 35(3): 335-347.
  19. Rodriguez P, Wohlberg B. A MATLAB implementation of a fast incremental principal component pursuit algorithm for video background modeling. 2014 IEEE Int Conf on Image Processing (ICIP) 2014: 3414-3416.
  20. Rodriguez P, Wohlberg B. Fast principal component pursuit via alternating minimization. 2013 IEEE Int Conf on Image Processing 2013: 69-73.
  21. Zheng J, Wang Y, Nihan NL, Hallenbeck ME. Extracting roadway background image: Mode-based approach. Transp res rec 2006; 1944(1): 82-88.
  22. Oliver NM, Rosario B, Pentland AP. A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 2000; 22(8): 831-843.
  23. Maddalena L, Petrosino A. A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 2008; 17(7): 1168-1177.
  24. Wright J, Ganesh A, Rao S, Ma Y. Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. Coordinated Science Laboratory Report no UILU-ENG-09-2210 2009: DC-243.
  25. Rodriguez P, Wohlberg B. An incremental principal component pursuit algorithm via projections onto the ℓ 1 ball. 2017 IEEE XXIV Int Conf on Electronics, Electrical Engineering and Computing (INTERCON) 2017: 1-4.
  26. Brand M. Fast low-rank modifications of the thin singular value decomposition. Linear Algebra Appl 2006; 415(1): 20-30.
  27. Feng J, Xu H, Yan S. Online robust PCA via stochastic optimization. Proc 26th Int Conf on Neural Information Processing Systems (NIPS'13) 2013; 1: 404-412.
  28. Echebest N, Sánchez MD, Schuverdt ML. Convergence results of an augmented Lagrangian method using the exponential penalty function. J Optim Theory Appl 2016; 168(1): 92-108.
  29. Bertsekas DP. Constrained optimization and Lagrange multiplier methods. Academic Press; 2014.
  30. Goyette N, Jodoin PM, Porikli F, Konrad J, Ishwar P. Changedetection.net: A new change detection benchmark dataset. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2012: 1-8.
  31. Wang Y, Jodoin PM, Porikli F, Konrad J, Benezeth Y, Ishwar P. CDnet 2014: An expanded change detection benchmark dataset. Proc IEEE Conf on Computer Vision and Pattern Recognition Workshops 2014: 387-394.
  32. Khare M, Srivastava RK, Khare A. Moving object segmentation in Daubechies complex wavelet domain. Signal Image Video Process 2015; 9(3): 635-650.
  33. Dou J, Qin Q, Tu Z. Background subtraction based on deep convolutional neural networks features. Multimed Tools Appl 2019; 78(11): 14549-14571.
  34. Sengar SS, Mukhopadhyay S. Moving object detection using statistical background subtraction in wavelet compressed domain. Multimed Tools Appl 2020; 79(9): 5919-5940.
  35. Martin C. Background subtraction using running gaussian average: A color channel comparison. Seminar aus Bildverarbeitung und Mustererkennung 2014: 0625040.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20