Authors:
Naoki Nishida
1
;
Yasutomo Kawanishi
1
;
Daisuke Deguchi
2
;
Ichiro Ide
1
;
Hiroshi Murase
1
and
Jun Piao
3
Affiliations:
1
Graduate School of Informatics, Nagoya University, Aichi, Japan
;
2
Information Strategy Office, Nagoya University, Aichi, Japan
;
3
Data Science Research Laboratories, NEC Corporation, Kanagawa, Japan
Keyword(s):
Skeleton Orientation Alignment, Skeleton Representation Sequence, White Cane User Recognition.
Abstract:
Recently, various facilities have been deployed to support visually impaired people. However, accidents caused by visual disabilities still occur. In this paper, to support the visually impaired people in public areas, we aim to identify the presence of a white cane user from a surveillance camera by analyzing the temporal transition of a human skeleton in a pedestrian image sequence represented as 2D coordinates. Our previously proposed method aligns the orientation of the skeletons to various orientations and identifies a white cane user from the corresponding sequences, relying on multiple classifiers related to each orientation. The method employs an exemplar-based approach to perform the alignment, and heavily depends on the number of exemplars and consumes excessive memory. In this paper, we propose a method to align 2D skeleton representation sequences to various orientations using the proposed Skeleton Orientation Alignment Networks (SOANets) based on an encoder-decoder model
. Using SOANets, we can obtain 2D skeleton representation sequences aligned to various orientations, extract richer skeleton features, and recognize white cane users accurately. Results of an evaluation experiment shows that the proposed method improves the recognition rate by 16%, compared to the previous exemplar-based method.
(More)