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Human height estimation using visual geometry and feature learning

Author(s)
Dokthurian, Siriporn
Rattanapitak, Wirat
Wangsiripitak, Somkiat
Date Issued
January 1, 2021
Type
Conference Paper
DOI
10.1109/ISCAS51556.2021.9401250
Abstract
Many existing video surveillance systems use human characteristics like face, height, and gait to identify a person. This paper proposes a human height estimation approach using visual geometry and feature learning that makes an estimate from a video clip of a person. An experiment was conducted to evaluate the performance of the approach. The approach achieved an average percentage final height estimate of 100.59 % (actual height = 100%), better than a previously reported estimate of 98.8% in the literature achieved by another approach. A successful further development of this approach would directly benefit forensic science investigators.
Citation
Proceedings IEEE International Symposium on Circuits and Systems, 2021-May, 2021
Subjects

Camera calibration

Height estimation

Machine learning

Single camera view

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