Volume 7, Issue 3, December 2020, Pages 152 - 155
Estimation of Self-Posture of a Pedestrian Using MY VISION Based on Depth and Motion Network
Joo Kooi Tan1, *, Tomoyuki Kurosaki2
1Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensuicho, Tobata, Kitakyushu, Fukuoka 804-8550, Japan
2Graduate School of Engineering, Kyushu Institute of Technology, 1-1 Sensuicho, Tobata, Kitakyushu, Fukuoka 804-8550, Japan
*Corresponding author. Email: email@example.com
Joo Kooi Tan
Received 10 November 2019, Accepted 18 May 2020, Available Online 11 September 2020.
- 10.2991/jrnal.k.200909.002How to use a DOI?
- Posture; posture analysis; pedestrian; MY VISION; depth and motion network
A system is proposed that performs gait analysis of a pedestrian to improve a walk posture and at the same time to prevent a fall. In the system, a user walks with a chest-mounted camera. His/her walking posture is estimated using a pair of images obtained from the camera. Normally it is difficult to estimate the camera movement, when the parallax of the image pair is small. Therefore, the system uses a convolutional neural network. Optical flow and camera movement, and depth images are estimated alternately. Satisfactory results were obtained experimentally.
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Joo Kooi Tan AU - Tomoyuki Kurosaki PY - 2020 DA - 2020/09/11 TI - Estimation of Self-Posture of a Pedestrian Using MY VISION Based on Depth and Motion Network JO - Journal of Robotics, Networking and Artificial Life SP - 152 EP - 155 VL - 7 IS - 3 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.200909.002 DO - 10.2991/jrnal.k.200909.002 ID - Tan2020 ER -