Syed Sohaib Ali
ABSTRACT
This paper presents a robust adaptive moving human detection and recognition method in videos. The human detection method consists of modified moving average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The moving average background model is used for background modeling and the background subtraction system is used to provide foreground image through difference image between current image and background model. The adaptive threshold method is used to simultaneously update the system to environment changes. The modified human model consists of five parts with robust features to facilitate human recognition process. For recognition purpose Support Vector Machine has been used as classifier. Experimental results show the effectiveness of proposed system.
- CAVIAR 2003 data set: http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/Google Scholar
- Zui Zhang, Hat ice Gunes, Massimo Piccardi. "Tracking People In crowd by a Part Matching Approach", IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance. 2008 Google ScholarDigital Library
- Jain, R. and Nagel, H. 1979. "On the analysis of accumulative difference pictures from image sequences of real world scenes". IEEE Transaction on Pattern Analysis and Machine Intelligence. 1, 2, 206--214.Google Scholar
- Alper Yilmaz, Omar Javed, Mubarak Shah "Object Tracking: A Survey" ACM Computing Surveys, Vol. 38, No. 4, Article 13, Publication date: December 2006. Google ScholarDigital Library
- C. R. Wren, A. Azarbayejani, T. Darrell, A. Penyland, "Pfinder: Real-time tracking of the human body", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): pp. 780--785. Google ScholarDigital Library
- Stauffer C, Grimson W. "Adaptive background mixture models for real-time tracking". In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Fort Collins, Colorado, USA, IEEE, 1999: 245--252Google ScholarCross Ref
- Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction", In: Proceedings of the 17th International Conference on Patter Recognition, Cambridge, United Kingdom, IEEE, 2004, 2: pp. 28--31. Google ScholarDigital Library
- Stauffer C, Grimson W. E. L., "Learning patterns of activity using real-time tracking". IEEE Transactions on Pattern Analysis & Machine Intelligence, 2000. 22(8): p. 747--57. Google ScholarDigital Library
- Shi Shi-xu, Zheng Qi-lun, Huang Han. "A Fast Algorithm for Real time Video Tracking". IEEE Workshop on intelligent Information Technology Application 2007. Google ScholarDigital Library
- Manjunath Narayana "Automatic Tracking of Moving Objects in Video for Surveillance Applications" M Eng thesis University of Kansas, July 2007.Google Scholar
- P. KaewTraKulPong and R. Bowden, "An improved adaptive background mixture model for real-time tracking and shadow detection", Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 1--5, 2001.Google Scholar
- A. Prati and et al., "Detecting moving shadows: Algorithms and evaluation," IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(7): 918--923, 2003. Google ScholarDigital Library
- N. Friedman, S. Russell, "Image segmentation in video sequences: a probabilistic approach", In: Proceeding of Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, Rhode Island, USA, Morgan Kaufmann Publishers, 1997, pp. 175--181. Google ScholarDigital Library
- A. Mittal, N. Paragios, "Motion-based background subtraction using adaptive kernel density estimation", In: Proceedings of IEEE conference on Computer Vision and Patter Recognition. Washington D C, USA, IEEE, 2004, pp. 302--309. Google ScholarDigital Library
- DTREG http://www.dtreg.com/svm.htmGoogle Scholar
- C. Wren, A. Azarbayejani, T. Darrell and A. Pentland, "Pfinder: Real time tracking of human body", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 19, 1997, pp. 780--785. Google ScholarDigital Library
- I. Haritaoglu, D. Harwood and L. S. Davis, "W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People", In Proc of the International Conference on Face and Gesture Recognition, April 1998 Google ScholarDigital Library
- S. J McKenna, S. Jabri Z. Duric, A. Rosenfeld, and H. Wechsler, "Tracking Groups of People", Computer Vision and Image Understanding 80:42--56, 2000.Google ScholarDigital Library
- J. Connell, A. W. Senoir, A, Hampapur, Y-L, Tian, L Brown and S. Pankanti, "Detection and Tracking in the IBM People Vision System", IEEE ICME, June 2004.Google ScholarCross Ref
- L. M Fuentes and S. A Velastin. "People Tracking in surveillance application", in Proc 2nd IEEE International Workshop on PETS Dec. 2001.Google Scholar
- M Jordan, J Kleinberg, B Scholkopf, Support Vector Machine, Springer.Google Scholar
- Simon Haykin, Neural Network, 2nd edition Pearson EducationGoogle Scholar
- John Shawe-Taylor & Nello Cristianini Support Vector Machines and other kernel-based learning methods - Cambridge University Press, 2000 Google ScholarDigital Library
- Park, S. and Aggarwal, J. K. Segmentation and tracking of interacting human body parts under occlusion and shadowing. In MOTION '02: Proceedings of the Workshop on Motion and Video Computing, pages 105--111, Washington, DC, USA. IEEE Computer Society. Google ScholarDigital Library
Index Terms
- Moving human detection and recognition in videos using adaptive method and support vector machine
Recommendations
Detection and Recognition of Human in Videos Using Adaptive Method and Neural Net
SOCPAR '09: Proceedings of the 2009 International Conference of Soft Computing and Pattern RecognitionDetection and recognition of the moving objects in dynamic environment is difficult task. This paper presents a modified framework for the detection and recognition of moving people in videos. Detection part of the proposed method consists of average ...
Binary Descriptor Based Nonparametric Background Modeling for Foreground Extraction by Using Detection Theory
Recently, most background modeling approaches represent distributions of background changes by using parametric models such as Gaussian mixture models. Because of significant illumination changes and dynamic moving backgrounds with time, variations of ...
Future-data driven modeling of complex backgrounds using mixture of Gaussians
Mixture of Gaussians (MoG) is well-known for effectively in sustaining background variations, which has been widely adopted for background subtraction. However, in complex backgrounds, MoG often traps in keeping balance between model convergence speed ...
Comments