|Progressive Filtering Approach for Early Human Action Recognition
Tehao Zhu, Yue Zhou, Zeyang Xia*, Jiaqi Dong, and Qunfei Zhao
International Journal of Control, Automation, and Systems, vol. 16, no. 5, pp.2393-2404, 2018
Abstract : "Human action recognition plays an important role in vision-based human-robot interaction (HRI). In
many application scenarios of HRI, robot is required to recognize the human action expressions as early as possible
in order to ensure a suitable response. In this paper, we proposed a novel progressive filtering approach to improve
the robot’s performance in identifying the ongoing human actions and thus to enhance the fluency and friendliness
of HRI. Human movement data were captured by a Kinect device, and then the human actions were constituted by
the refined movement data using robust regression-based refinement. Motion primitive, including both spatial and
temporal information concerning the movement, was considered as an improved representation of action features.
Then, the early human action recognition was accomplished based on an improved locality-sensitive hashing algorithm,
by which the ongoing input action can be classified progressively. The proposed approach has been evaluated
on four datasets of human actions in terms of accuracy and recall curves. The experiments showed that the proposed
progressive filtering approach achieves high recognition rate, and in addition, can make the recognition decision at
an earlier stage of the ongoing action."
"Early human action recognition, human-robot interaction, locality-sensitive hashing, motion primitive, progressive filter."
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