TY - GEN
T1 - Feature integration with random forests for real-time human activity recognition
AU - Kataoka, Hirokatsu
AU - Hashimoto, Kiyoshi
AU - Aoki, Yoshimitsu
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.
AB - This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.
KW - Activity Recognition
KW - Feature Integration
KW - Random Forests
UR - http://www.scopus.com/inward/record.url?scp=84924368596&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924368596&partnerID=8YFLogxK
U2 - 10.1117/12.2181201
DO - 10.1117/12.2181201
M3 - Conference contribution
AN - SCOPUS:84924368596
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Seventh International Conference on Machine Vision, ICMV 2014
A2 - Vuksanovic, Branislav
A2 - Zhou, Jianhong
A2 - Verikas, Antanas
A2 - Radeva, Petia
PB - SPIE
T2 - 7th International Conference on Machine Vision, ICMV 2014
Y2 - 19 November 2014 through 21 November 2014
ER -