Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Interaction Recognition Through Body Parts Relation Reasoning
AU - Perez, M.
AU - Liu, Jun
AU - Kot, A.C.
PY - 2020/2/23
Y1 - 2020/2/23
N2 - Person-person mutual action recognition (also referred to as interaction recognition) is an important research branch of human activity analysis. It begins with solutions based on carefully designed local-points and hand-crafted features, and then progresses to deep learning architectures, such as CNNs and LSTMS. These solutions often consist of complicated architectures and mechanisms to embed the relationships between the two persons on the architecture itself, to ensure the interaction patterns can be properly learned. Our contribution with this work is by proposing a more simple yet very powerful architecture, named Interaction Relational Network, which utilizes minimal prior knowledge about the structure of the data. We drive the network to learn to identify how to relate the body parts of the persons interacting, in order to better discriminate among the possible interactions. By breaking down the body parts through the frames as sets of independent joints, and with a few augmentations to our architecture to explicitly extract meaningful extra information from each pair of joints, our solution is able to achieve state-of-the-art performance on the traditional interaction recognition dataset SBU, and also on the mutual actions from the large-scale dataset NTU RGB+D.
AB - Person-person mutual action recognition (also referred to as interaction recognition) is an important research branch of human activity analysis. It begins with solutions based on carefully designed local-points and hand-crafted features, and then progresses to deep learning architectures, such as CNNs and LSTMS. These solutions often consist of complicated architectures and mechanisms to embed the relationships between the two persons on the architecture itself, to ensure the interaction patterns can be properly learned. Our contribution with this work is by proposing a more simple yet very powerful architecture, named Interaction Relational Network, which utilizes minimal prior knowledge about the structure of the data. We drive the network to learn to identify how to relate the body parts of the persons interacting, in order to better discriminate among the possible interactions. By breaking down the body parts through the frames as sets of independent joints, and with a few augmentations to our architecture to explicitly extract meaningful extra information from each pair of joints, our solution is able to achieve state-of-the-art performance on the traditional interaction recognition dataset SBU, and also on the mutual actions from the large-scale dataset NTU RGB+D.
U2 - 10.1007/978-3-030-41404-7_19
DO - 10.1007/978-3-030-41404-7_19
M3 - Conference contribution/Paper
SN - 9783030414030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 268
EP - 280
BT - Pattern Recognition
A2 - Palaiahnakote, S.
A2 - Sanniti di Baja, G.
A2 - Wang, L.
A2 - Yan, W.
PB - Springer
CY - Cham
ER -