A human data-driven interaction estimation using IoT sensors for workplace design

Hoon, Jae; Hyun, Seung

doi:10.1016/j.autcon.2020.103352

Cited by 10 publications

(2 citation statements)

References 63 publications

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“…We have also observed that the current trend [17][18][19][20][21] of tackling the problem of multiperson interactive activity recognition is to develop a model or framework with increasing complexity to jointly learn more subtasks simultaneously (e.g., detection, tracking, pose estimation, appearance modelling, and interaction).…”

Section: Journal Of Mathematicsmentioning

confidence: 99%

“…In order to recognize the interaction relationship of multiperson behavior, we developed a multiperson relation method in which we defined three indexes [19]: movement time (MT), nonoverlapped movement time (NOMT), and group movement time (GMT). MT denotes the movement time of a single user, GMT is the total movement time of all persons, and NOMT is the nonoverlapped movement time between persons.…”

Section: Interaction Estimationmentioning

confidence: 99%

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Multiperson Interactive Activity Recognition Based on Interaction Relation Model

Chi

et al. 2021

Journal of Mathematics

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Multiperson activity recognition is a pivotal branch as well as a challenging topic of human action recognition research. This paper adopts a hybrid learning model to the spatio-temporal relationship and occlusion relationship among multiple people. Initially, this paper builds up an active multiperson interaction relationship estimation framework model to capture interpersonal spatio-temporal relation. This model incorporates the interaction relationship estimation framework with the multiperson relationship network. On this ground, it automatically learns from the human-computer interaction dataset in an end-to-end manner and performs reasoning with standard matrix operations. Secondly, this paper proposed an adaptive occlusion state behavior recognition method derived from the semantic knowledge model to ravel out the concern of occlusion and self-occlusion in human action recognition. Then, Petri Nets are used to recognize multiperson interactive actions. This model has been through extensive experiments on the TV interaction dataset, Vlog dataset, AVA dataset, and MLB-YouTube dataset, experimental results have proved that the recognition performance of this model is superior than the other available models. This paper prospects and summarizes the estimation framework of the interaction relationship and occlusion semantic-knowledge relationship. Experimental results suggest that the proposed method in the paper could capture the discriminative relation information for multiperson interactive activity recognition, which further validates the efficiency of the hybrid learning model.

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