Vol. 6 No. 1 (2019)

Articles

NI LabVIEW Based Camera System Used for Gait Detection

Published:

2019-12-23

https://doi.org/10.17667/riim.2019.1/6.

Authors

Dániel Dániel Salánki,

University of Debrecen, Faculty of Engineering, Department of Mechatronics

Kornél Sarvajcz

University of Debrecen, Faculty of Engineering, Department of Mechatronics

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Keywords

camera system gait image identification integral calculus evaluation

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Copyright (c) 2019 by the authors

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Selected Style: APA

Dániel Salánki, D., & Sarvajcz, K. (2019). NI LabVIEW Based Camera System Used for Gait Detection. Recent Innovations in Mechatronics, 6(1), 1-3. https://doi.org/10.17667/riim.2019.1/6.

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Abstract

In these times, with the development of the world, biometric identification systems are becoming more and more widespread. Access control systems, but even the most mid-range smartphones have biometric authentication features, and even ID cards can include a person's fingerprint. The research group previously realized a rudimentary gait recognition system, which was upgraded to a multicamera system with high-resolution cameras and instead of reference points, the new version recognizes different templates. The program can compare and evaluate the functions that are matched to the reference curve and the current curve in a specific way, whether two walking images are identical. The comparison is decided by the definite integrals of the two suited functions. The self-developed gait recognition system was tested by the research team on several test subjects and according to the results, permission was never given to a strange person.

References

1. D. Viktor, „Optikai alapú Motion Capture rendszer,” pp. 5, 7-9, 2011.
2. Mark S. Nixon, T. N. Tan, R. Chellappa, „Human Identification Based on Gait,” New York, Springer Science + Business Media, Inc., 2006., p. 11.
3. Ju Han, Bir Bhanu, „Individual Recognition Using Gate Energy Image,” IEEE transactions on pattern analysis and machine intelligence, %1. szám28.2, pp. 316-322, 2006.
4. Wei Zeng, Cong Wang, „Neurocomputing - View-invariant gait recognition via deterministic learning,” ,175, pp. 324-335, 2016.
5. Salánki Dániel, Sarvajcz Kornél, Dr. Géza Husi: “Járásfelismerés fejlesztése NI LabVIEW környezetben”, Debrecen, University of Debrecen, Mechatronical Engineering BSc, thesis, 2018.
6. Salánki Dániel, „Járásfelismerés fejlesztése NI LabVIEW környezetben,” University of Debrecen, Faculty of Engineering,
7. National Council of Student Research Societies, Debrecen, 2017.
8. Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank, „General Tensor Discriminant Analysis and Gabor Features for Gait
9. Recognition,” IEEE transactions on pattern analysis and machine intelligence, pp. 1-35, 2007.
10. Khalid Bashir, Tao Xiang, Shaogang Gong, „Gait recognition without subject cooperation,” Pattern Recognition Letters, pp. 2052-2060, 2010.
11. Chin Poo Lee, Alan W.C. Tan, Shing Chiang Tan, „Gait recognition with Transient Binary Patterns,” J. Vis. Commun. Image R., pp. 69-77, 2015.
12. Gálai Bence, Benedek Csaba, „Járás alapú személyazonosítás és cselekvésfelismerés LiDAR szenzorokkal”, 2017.