Skeleton-based Action Recognition of People Handling Objects

21 Jan 2019 Sunoh Kim Kimin Yun Jongyoul Park Jin Young Choi

In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this problem, we propose a new framework for recognizing object-related human actions by graph convolutional networks using human and object poses... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Action Recognition ICVL-4 OHA-GCN (Two stream; HP + OHP-hands + informative samples) Accuracy 91.86% # 1
Action Recognition IRD OHA-GCN (Two stream; HP + OHP-hands + informative samples) Accuracy 80.11% # 1

Methods used in the Paper


METHOD TYPE
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