TY - GEN
T1 - Study on Improvement of Estimation Accuracy in Pose Estimation Model Using Time Series Correlation
AU - Yamakawa, Atsuya
AU - Ishikawa, Takaaki
AU - Watanabe, Hiroshi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/13
Y1 - 2020/10/13
N2 - Detecting human pose in a video is a difficult task. Although many high-performed human pose estimation models have been proposed in the last few years, the estimation accuracy has always been a major concern. In this study we present a method to improve the accuracy of human pose estimation for videos. Technically, predicted human pose is a set of time series data. Thus, by using time series correlation, human pose estimation can be performed in a better accuracy. We combine a CNN based human pose estimation model with a multiple object tracking framework to achieve this. Undetected/mis-detected body joints will be interpolated using the information from previous and following frames. As a result, our proposed method improved the accuracy of an existing CNN based human pose estimation model by reducing the number of undetected and mis-detected frames by 6.30% and 0.98% respectively.
AB - Detecting human pose in a video is a difficult task. Although many high-performed human pose estimation models have been proposed in the last few years, the estimation accuracy has always been a major concern. In this study we present a method to improve the accuracy of human pose estimation for videos. Technically, predicted human pose is a set of time series data. Thus, by using time series correlation, human pose estimation can be performed in a better accuracy. We combine a CNN based human pose estimation model with a multiple object tracking framework to achieve this. Undetected/mis-detected body joints will be interpolated using the information from previous and following frames. As a result, our proposed method improved the accuracy of an existing CNN based human pose estimation model by reducing the number of undetected and mis-detected frames by 6.30% and 0.98% respectively.
KW - Computer Vision
KW - Human Pose Estimation
KW - Multiple Object Tracking
UR - http://www.scopus.com/inward/record.url?scp=85099345721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099345721&partnerID=8YFLogxK
U2 - 10.1109/GCCE50665.2020.9291962
DO - 10.1109/GCCE50665.2020.9291962
M3 - Conference contribution
AN - SCOPUS:85099345721
T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
SP - 409
EP - 412
BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Y2 - 13 October 2020 through 16 October 2020
ER -