Object Recognition System Based on Oriented FAST and Rotated BRIEF
Majeed A. Mohsin ABDULMAJEED1, Levent Seyfi2*
1Konya Technical University, Konya, Turkey
2Konya Technical University, Konya, Turkey
* Corresponding author: leventseyfi@selcuk.edu.tr
Presented at the 2nd International Symposium on Innovative Approaches in Scientific Studies (ISAS2018-Winter), Samsun, Turkey, Nov 30, 2018
SETSCI Conference Proceedings, 2018, 3, Page (s): 179-181
Published Date: 31 December 2018
Over the last few years, object recognition systems take the researchers' attention. The revolution in the artificial intelligence and computer vision systems and algorithms drives to numerous approaches. In this research, we are presenting an object recognition system that used ORB (Oriented FAST and Rotated BRIEF) as a feature extraction method, the proposed approach used as a robot system that performs objects detection and recognition tasks. We used ORB to detect preset features in a given video frame obtained from the camera at real-time and then used Brute-force matcher to match a new video frame features with the object image that we want the robot to track it and recognize it. The matched point must have a distance ratio of 0.65.
Keywords - Object recognition, ORB, BRIEF, FAST, RC Robot, Feature matching, Brute-force Computer Vision
[1] M. Bennamoun, and G. Mamic. Object recognition: fundamentals and case studies. Springer Science & Business Media, 2002.
[2] P.J. Besl, and R.C. Jain, Three-dimensional object recognition, ACM Computing Surveys (CSUR) 17.1 (1985): 75-145.
[3] I. Biederman, Recognition-by-components: A theory of human image understanding. Psychological Review, 94(2):115-147, 1987.
[4] A. Opelt, A. Pinz, and A. Zisserman. A Boundary-Fragment Model for Object Detection. In Proc. ECCV, volume 2, pp 575–588, May 2006.
[5] B. Wu, and R. Nevatia. Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors, Tenth IEEE International Conference on Computer Vision (ICCV 2005), Vol. 1. 2005.
[6] A. Opelt, A. Pinz, A. Zisserman, Incremental Learning of Object Detectors Using a Visual Shape Alphabet, Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06).
[7] S. Belongie, J. Malik, And J. Puzicha, 2001. Matching shapes, In Proceedings of the IEEE International Conference on Computer Vision (ICCV’01).
[8] S. Belongie, J. Malik, and J. Puzicha, Shape Matching and Object Recognition Using Shape Contexts, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, no. 4, pp. 509-522, Apr. 2002.
[9] E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: An efficient alternative to SIFT or SURF, IEEE international conference on Computer Vision (ICCV), 2011.
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