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A Parametric Approach to Gait Signature Extraction for Human Motion Identification
Mohamed Rafi, Md. Ekramul Hamid, Mohamed Samiulla Khan, R.S.D Wahidabanu
Pages - 185 - 198     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Gait signature, Hough Transform, Canny Edge detection
The extraction and analysis of human gait characteristics using image sequences are currently an intense area of research. Identifying individuals using biometric methods has recently gained growing interest from computer vision researchers for security purposes at places like airport, banks etc. Gait recognition aims essentially to address this problem by identifying people at a distance based on the way they walk i.e., by tracking a number of feature points or gait signatures. We describe a new model-based feature extraction analysis is presented using Hough transform technique that helps to read the essential parameters used to generate gait signatures that automatically extracts and describes human gait for recognition. In the preprocessing steps, the picture frames taken from video sequences are given as input to Canny edge detection algorithm which helps to detect edges of the image by extracting foreground from background also it reduces the noise using Gaussian filter. The output from edge detection is given as input to the Hough transform. Using the Hough transform image, a clear line based model is designed to extract gait signatures. A major difficulty of the existing gait signature extraction methods are the good tracking the requisite feature points. In the proposed work, we have used five parameters to successfully extract the gait signatures. It is observed that when the camera is placed at 90 and 270 degrees, all the parameters used in the proposed work are clearly visible. The efficiency of the model is tested on a variety of body position and stride parameters recovered in different viewing conditions on a database consisting of 20 subjects walking at both an angled and frontal-parallel view with respect to the camera, both indoors and outdoors and find the method to be highly successful. The test results show good clarity rates, with a high level of confidence and it is suggested that the algorithm reported here could form the basis of a robust system for monitoring of gait.
CITED BY (5)  
1 Ketcham, M., & Ganokratanaa, T. (2015). The analysis of lane detection algorithms using histogram shapes and Hough transform. International Journal of Intelligent Computing and Cybernetics, 8(3), 262-278.
2 López-Nava, I. H., González, I., Muñoz-Meléndez, A., & Bravo, J. (2015). Comparison of a Vision-Based System and a Wearable Inertial-Based System for a Quantitative Analysis and Calculation of Spatio-Temporal Parameters. In Ambient Intelligence for Health (pp. 116-122). Springer International Publishing.
3 Bravo, J. (2015, December). Comparison of a Vision-Based System and a Wearable Inertial-Based System for a Quantitative Analysis and Calculation of Spatio-Temporal Parameters. In Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings (Vol. 9456, p. 116). Springer.
4 Htwe, N. N., & War, N. (2013). Human Identification Based Biometric Gait Features using MSRC. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2(5), pp-1879.
5 Rafi, M., Khammari, H., Wahidabanu, R. S. D., & Taj, Y. A Model Based Approach for Gait Recognition System.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Scribd 
6 SlideShare 
7 PdfSR 
Aristotle (350BC), On the Gait of Animals, Translated by A. S. L. Farquharson 2007.
B. James, H. Acquah, M. S. Nixon, J. N. Carter, ”Automatic gait recognition by symmetry analysis”, Image, Speech and Intelligent Systems Group, Department of Electronics and Computer Science, University of Southampton, Southampton, S017 1BJ, United Kingdom.
C. BenAbdelkader, R. Cutler, and L. Davis. “Motion-Based Recognition of People in EigenGait Space”, In Automated Face and Gesture Recognition, pages 267–272, May 2002.
C. Fahn, M. Kuo and M. Hsieh, ”A Human Gait Classification Method Based on Adaboost Techniques Using Velocity Moments and Silhouette Shapes”, Next-Generation Applied Intelligence, Volume 5579/2009, 535-544, DOI: 10.1007/978-3-642-02568, 2009.
D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice-Hall, India, 2003.
D. Cunado, M. S. Nixon, and J. N. Carter, Automatic extraction and description of human gait models for recognition purposes, Academic press, 2002.
D. Cunado, M. S. Nixon, and J. N. Carter, “Using gait as a biometric, via phase weighted magnitude spectra” first international conference on audio and video based biometric person authentication, pp. 95-102, 1997.
H. Murase and R. Sakai. “Moving object recognition in eigenspace representation: gait analysis and lip reading”,Pattern Recognition Letters, 17:155–162, 1996.
J. Little and J. Boyd. “Recognizing People by Their Gait: The Shape of Motion.”, Videre, 1(2):1–32, 1986.
J. M. Nash, J. N. Carter, and M. S. Nixon. “Extraction of Moving Articulated-Objects by Evidence Gathering”, In Lewis and Nixon [Lewis98], pp. 609–18. September 1998.
J. Rose, and J. Gamble, Human Walking, 3rd edition, New York: Lippencott Williams and Wilkins, 2006.
L. Wang, T. Tan, H. Ning, and W. Hu, “Silhouette Analysis-Based Gait Recognition for Human Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, December 2003.
Lu, A, Jiwen, Z. B. Erhu,.”Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion”, School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, 25 July 2007.
Q. He and C. Debrunner. “Individual recognition from periodic activity using Hidden Markov Models.”, In IEEE Workshop on Human Motion, Austin, Texas, December 2000.
S. Niyogi and E. Adelson. “Analyzing and recognizing walking figures in XYT.”, in Conference on Computer Vision and Pattern Recognition, pages 469–474, 1994.
S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. Bowyer, “The humanID gait challenge problem: Data sets,performance and analysis”, IEEE Trans.Pattern Anal. Mach. Intell., vol. 27, no. 2,pp. 162–177, Feb. 2005.
X. Han, ”Gait Recognition Considering Walking Direction”, University of Rochester, USA, August 20, 2010.
Mr. Mohamed Rafi
HMS Institute of Technology - India
Dr. Md. Ekramul Hamid
KKU - Saudi Arabia
Mr. Mohamed Samiulla Khan
- Saudi Arabia
Mr. R.S.D Wahidabanu
- India

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