Paper Info
Title
Human-Object Maps for Daily Activity Recognition
Abstract
In the field of action recognition, when and where an interaction between a human and an object happens has the potential to be valid information in enhancing action recognition accuracy. Especially, in daily life where each activities are performed in longer time frame, conventional short term action recognition may fail to generalize do to the variety of shorter actions that could take place during the activity. In this paper, we propose a novel representation of human object interaction called Human-Object Maps (HOMs) for recognition of long term daily activities. HOMs are 2D probability maps that represents spatio-temporal information of human object interaction in a given scene. We analyzed the effectiveness of HOMs as well as features relating to the time of the day in daily activity recognition. Since there are no publicly available daily activity dataset that depicts daily routines needed for our task, we have created a new dataset that contains long term activities. Using this dataset, we confirm that our method enhances the prediction accuracy of the conventional 3D ResNeXt action recognition method from 86.31% to 97.89%.
Year
DOI
Venue
2019
10.23919/MVA.2019.8757896
2019 16th International Conference on Machine Vision Applications (MVA)
Keywords
Field
DocType
time frame,spatio-temporal information,conventional 3D ResNeXt action recognition method,publicly available daily activity dataset,2D probability maps,long term daily activities,HOMs,human object interaction,conventional short term action recognition,action recognition accuracy,daily activity recognition,Human-Object Maps
Activities of daily living,Activity recognition,Pattern recognition,Time frame,Action recognition,Speech recognition,Artificial intelligence,Mathematics
Conference
ISBN
Citations 
PageRank 
978-1-7281-0925-1
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Haruya Ishikawa100.34
Yuchi Ishikawa201.35
Shuichi Akizuki3114.62
Yoshimitsu Aoki48023.65