Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF

Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform...

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Main Authors: Mohd Suaib, Norhayati, Marhaban, Mohammad Hamiruce, Saripan, M. Iqbal, Ahmad, Siti Anom
Format: Conference or Workshop Item
Language:English
Published: IEEE 2014
Online Access:http://psasir.upm.edu.my/id/eprint/38886/1/Performance%20evaluation%20of%20feature%20detection%20and%20feature%20matching%20for%20stereo%20visual%20odometry%20using%20SIFT%20and%20SURF.pdf
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author Mohd Suaib, Norhayati
Marhaban, Mohammad Hamiruce
Saripan, M. Iqbal
Ahmad, Siti Anom
author_facet Mohd Suaib, Norhayati
Marhaban, Mohammad Hamiruce
Saripan, M. Iqbal
Ahmad, Siti Anom
author_sort Mohd Suaib, Norhayati
collection UPM
description Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) performances. The results show that SURF is outperform than SIFT in term of rate of matched points and also in computational time.
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spelling upm.eprints-388862018-10-31T00:46:26Z http://psasir.upm.edu.my/id/eprint/38886/ Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF Mohd Suaib, Norhayati Marhaban, Mohammad Hamiruce Saripan, M. Iqbal Ahmad, Siti Anom Feature detection and feature matching are the most crucial parts in visual odometry process. In order to suit the real time process in visual odometry, both of the stages must be robust but at the same time are fast to compute. This paper presents the evaluation of Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) performances. The results show that SURF is outperform than SIFT in term of rate of matched points and also in computational time. IEEE 2014 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38886/1/Performance%20evaluation%20of%20feature%20detection%20and%20feature%20matching%20for%20stereo%20visual%20odometry%20using%20SIFT%20and%20SURF.pdf Mohd Suaib, Norhayati and Marhaban, Mohammad Hamiruce and Saripan, M. Iqbal and Ahmad, Siti Anom (2014) Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF. In: 2014 IEEE Region 10 Symposium, 14-16 Apr. 2014, Kuala Lumpur, Malaysia. (pp. 200-203). 10.1109/TENCONSpring.2014.6863025
spellingShingle Mohd Suaib, Norhayati
Marhaban, Mohammad Hamiruce
Saripan, M. Iqbal
Ahmad, Siti Anom
Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
title Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
title_full Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
title_fullStr Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
title_full_unstemmed Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
title_short Performance evaluation of feature detection and feature matching for stereo visual odometry using SIFT and SURF
title_sort performance evaluation of feature detection and feature matching for stereo visual odometry using sift and surf
url http://psasir.upm.edu.my/id/eprint/38886/1/Performance%20evaluation%20of%20feature%20detection%20and%20feature%20matching%20for%20stereo%20visual%20odometry%20using%20SIFT%20and%20SURF.pdf
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