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Reseach Article

Performance Analysis of Various Feature Detector and Descriptor for Real-Time Video based Face Tracking

by Akash Patel, D. R. Kasat, Sanjeev Jain, V. M. Thakare
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 1
Year of Publication: 2014
Authors: Akash Patel, D. R. Kasat, Sanjeev Jain, V. M. Thakare
10.5120/16183-5415

Akash Patel, D. R. Kasat, Sanjeev Jain, V. M. Thakare . Performance Analysis of Various Feature Detector and Descriptor for Real-Time Video based Face Tracking. International Journal of Computer Applications. 93, 1 ( May 2014), 37-41. DOI=10.5120/16183-5415

@article{ 10.5120/16183-5415,
author = { Akash Patel, D. R. Kasat, Sanjeev Jain, V. M. Thakare },
title = { Performance Analysis of Various Feature Detector and Descriptor for Real-Time Video based Face Tracking },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 1 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number1/16183-5415/ },
doi = { 10.5120/16183-5415 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:14:43.604629+05:30
%A Akash Patel
%A D. R. Kasat
%A Sanjeev Jain
%A V. M. Thakare
%T Performance Analysis of Various Feature Detector and Descriptor for Real-Time Video based Face Tracking
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 1
%P 37-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the performance analysis of various contemporary feature detector and descriptor pair for real time face tracking. These feature detectors/descriptors are mostly used in image matching applications. Some feature detectors/descriptors like STAR, FAST, BRIEF, FREAK, and ORB can also be used for SLAM applications due to their high performance. However using only one of these feature detectors for object tracking may not provide good accuracy due to various challenges in tracking like abrupt change in object motion, non-rigid object structure, change in appearance of object, occlusions in the scene and camera motion. But it can be combined other object tracking algorithm to improve the overall tracking accuracy. In this paper we have measured the tracking speed and accuracy of these feature detectors in real time video for face tracking using parameters like average number of detected key points, average detection time of key-point, frame per second and number of matches using OpenCV.

References
  1. W. Hu, T. Tan, L. Wang, and S. Maybank, "A survey on visual surveillance of object motion and behaviors", IEEE Trans. Syst. Man Cyber. -C vol. 34 (3), 2004, pp. 334–352.
  2. A. Yilmaz, O. Javed, and M. Shah. Object tracking: A survey. ACM Computing Survey, vol. 38(4), 2006.
  3. J. Davison, I. Reid, N. Molton, and O. Stasse, "MonoSLAM: Real-Time Single Camera SLAM", IEEE Trans. PAMI, vol. 29(6), 2007, pp. 1052-1067.
  4. A. Schmidt, A. Kasi?ski, "The Visual SLAM System for a Hexapod Robot", Lecture Notes in Computer Science, vol. 6375, 2010, pp. 260–267.
  5. D. Lowe, "Object recognition from local scale-invariant features", in: Proceedings of the International Conference on Computer Vision ICCV, Corfu, 1999, pp. 1150–1157.
  6. E. Rosten, and T. Drummond, "Machine learning for highspeed corner detection", in Proc. of European Conf. on Computer Vision, 2006, pp. 430–443.
  7. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding, vol. 110(3), 2008, pp. 346–359.
  8. M. Agrawal, K. Konolige, and M. R. Blas, "CenSurE: Center surround extremas for real time feature detection and matching", Lecture Notes in Computer Science, vol. 5305, 2008, pp. 102–115.
  9. M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "BRIEF: Binary Robust Independent Elementary Features", in Proceedings of ECCV 2010, pp. 778–792.
  10. E. Rublee, V. Rabaud, K. Konolige, and G. R. Bradski, "ORB: An efficient alternative to SIFT or SURF", in Proc. ICCV, 2011, pp. 2564–2571.
  11. S. Leutenegger, M. Chli, and R. Siegwart, "Brisk: Binary robust invariant scalable keypoints," in Proc. Int. Conf. Computer Vision, 2011, pp. 2548–2555.
  12. E. Mair, G. D. Hager, D. Burschka, M. Suppa, and G. Hirzinger, "Adaptive and generic corner detection based on the accelerated segment test", In Proceedings of the European Conference on Computer Vision (ECCV), 2010.
  13. A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 510-517.
  14. Schaeffer, Cameron. "A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK", 2013.
  15. O. Martínez, A. Gil, M. Ballesta, and O. Reinoso, "Interest Point Detectors for Visual SLAM", In Current Topics in Artificial Intelligence, Springer Berlin Heidelberg, 2007, pp. 170-179.
  16. M. Ballesta, A. Gil, O. Martínez, and O. Reinoso, "Local Descriptors for Visual SLAM", in Proc. Workshop on Robotics and Mathematics, 2007.
  17. Quinlan, J. R. , "Induction of decision tree", Machine learning, vol. 1(1) 1986, pp. 81-106.
Index Terms

Computer Science
Information Sciences

Keywords

Face tracking Feature detectors and Feature descriptors.