Using a single input to forecast human action keystates in everyday pick and place actions
Author(s)
Bin Razali, Muhammad Haziq
Demiris, Yiannis
Type
Conference Paper
Abstract
We define action keystates as the start or end of an action
that contains information such as the human pose and time.
Existing methods that forecast the human pose use recurrent
networks that input and output a sequence of poses. In this pa-
per, we present a method tailored for everyday pick and place
actions where the object of interest is known. In contrast to
existing methods, ours uses an input from a single timestep to
directly forecast (i) the key pose the instant the pick or place
action is performed and (ii) the time it takes to get to the pre-
dicted key pose. Experimental results show that our method
outperforms the state-of-the-art for key pose forecasting and
is comparable for time forecasting while running at least an
order of magnitude faster. Further ablative studies reveal the
significance of the object of interest in enabling the total num-
ber of parameters across all existing methods to be reduced by
at least 90% without any degradation in performance.
that contains information such as the human pose and time.
Existing methods that forecast the human pose use recurrent
networks that input and output a sequence of poses. In this pa-
per, we present a method tailored for everyday pick and place
actions where the object of interest is known. In contrast to
existing methods, ours uses an input from a single timestep to
directly forecast (i) the key pose the instant the pick or place
action is performed and (ii) the time it takes to get to the pre-
dicted key pose. Experimental results show that our method
outperforms the state-of-the-art for key pose forecasting and
is comparable for time forecasting while running at least an
order of magnitude faster. Further ablative studies reveal the
significance of the object of interest in enabling the total num-
ber of parameters across all existing methods to be reduced by
at least 90% without any degradation in performance.
Date Issued
2022-04-27
Date Acceptance
2022-01-21
Citation
2022, pp.3488-3492
Publisher
IEEE
Start Page
3488
End Page
3492
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/abstract/document/9747710
Source
IEEE International Conference on Acoustics, Speech and Signal Processing
Publication Status
Published
Start Date
2022-05-23
Finish Date
2022-05-13
Coverage Spatial
Virtual
Date Publish Online
2022-04-27