NDLI: Feature-level combination of skeleton joints and body parts for accurate aggressive and agitated behavior recognition

Content Provider	Springer Nature Link
Author	Chikhaoui, Belkacem Ye, Bing Mihailidis, Alex
Copyright Year	2016
Abstract	This paper presents a novel and practical approach for aggressive and agitated behavior recognition using skeleton data. Our approach is based on feature-level combination of joint-based features and body part-based features. To characterize spatiotemporal information, our approach extracts first meaningful joint-based features by computing pairwise distances of skeleton 3D joint positions at each time frame. Then, distances between body parts as well as joint angles are computed to incorporate body part features. These features are then effectively combined using an ensemble learning method based on rotation forests. A singular value decomposition method is used for feature selection and dimensionality reduction. The proposed approach is validated using extensive experiments on variety of challenging 3D action datasets for human behavior recognition. We empirically demonstrate that our proposed approach accurately discriminates between behaviors and performs better than several state of the art algorithms.
Starting Page	957
Ending Page	976
Page Count	20
File Format	PDF
ISSN	18685137
Journal	Journal of Ambient Intelligence and Humanized Computing
Volume Number	8
Issue Number	6
e-ISSN	18685145
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-10-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational Intelligence Artificial Intelligence (incl. Robotics) Robotics and Automation User Interfaces and Human Computer Interaction
Content Type	Text
Resource Type	Article
Subject	Computer Science

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