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

Citation

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).

Abstract

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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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Engineering
DOI Number:	https://doi.org/10.1109/TENCONSpring.2014.6863025
Publisher:	IEEE
Keywords:	Visual odometry; SIFT; SURF; Feature detection; Feature matching
Depositing User:	Nursyafinaz Mohd Noh
Date Deposited:	19 Jun 2015 02:43
Last Modified:	31 Oct 2018 00:46
Altmetrics:	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6863025
URI:	http://psasir.upm.edu.my/id/eprint/38886
Statistic Details:	View Download Statistic

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