Title: A performance analysis of stereo matching algorithms for stereo vision applications in smart environments
Authors: V. Kavitha; G. Balakrishnan
Addresses: Department of Computer Science, Swami Dayananda College of Arts and Science, Manjakkudi, India ' Department of Computer Science and Engineering, Indira Ganesan College of Engineering, Trichy, India
Abstract: Stereo vision is a subfield of computer vision that tends to an essential research issue of reproducing the three-dimensional directions and focuses for depth estimation. This paper gives a relative investigation of stereo vision and matching techniques, utilised to resolve the correspondence problem. The investigation of matching algorithms is done by the use of extensive experiments on the Middlebury benchmark dataset. The tests concentrated on an examination of three stereovision techniques namely mean shift algorithm (MSA), seed growing algorithm (SGA) and multi-curve fitting (MCF) algorithm. With a specific end goal to evaluate the execution, some statistics related insights were computed. The experimental results demonstrated that best outcome is attained by the MCF algorithm in terms of depth estimation, disparity estimation and CT. The presented MCF algorithm attains a minimum computation time (CT) of 2 s whereas the other MSA and SGA require a maximum CT of 8.9 s and 7 s, respectively. The simulation results verified that the MCF algorithm reduces the processing time in a significant way than the compared methods.
Keywords: stereo matching; stereo vision; multi-fitting; Middlebury; smart cities.
Electronic Government, an International Journal, 2020 Vol.16 No.1/2, pp.210 - 221
Received: 12 Apr 2019
Accepted: 29 May 2019
Published online: 22 Feb 2020 *