title: Multipe/single-view human action recognition via part-induced multitask structural learning.
abstract: This paper proposes a unified framework for multiple/single-view human action recognition. First, we propose the hierarchical partwise bag-of-words representation which encodes both local and global visual saliency based on the body structure cue. Then, we formulate the multiple/single-view human action recognition as a part-regularized multitask structural learning (MTSL) problem which has two advantages on both model learning and feature selection: 1) preserving the consistence between the body-based action classification and the part-based action classification with the complementary information among different action categories and multiple views and 2) discovering both action-specific and action-shared feature subspaces to strengthen the generalization ability of model learning. Moreover, we contribute two novel human action recognition datasets, TJU (a single-view multimodal dataset) and MV-TJU (a multiview multimodal dataset). The proposed method is validated on three kinds of challenging datasets, including two single-view RGB datasets (KTH and TJU), two well-known depth dataset (MSR action 3-D and MSR daily activity 3-D), and one novel multiview multimodal dataset (MV-TJU). The extensive experimental results show that this method can outperform the popular 2-D/3-D part model-based methods and several other competing methods for multiple/single-view human action recognition in both RGB and depth modalities. To our knowledge, this paper is the first to demonstrate the applicability of MTSL with part-based regularization on multiple/single-view human action recognition in both RGB and depth modalities.
authors.forename: An-An
authors.initials: AA
authors.lastname: Liu
authors.forename: Yu-Ting
authors.initials: YT
authors.lastname: Su
authors.forename: Ping-Ping
authors.initials: PP
authors.lastname: Jia
authors.forename: Zan
authors.initials: Z
authors.lastname: Gao
authors.forename: Tong
authors.initials: T
authors.lastname: Hao
authors.forename: Zhao-Xuan
authors.initials: ZX
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006802:Human Activities / Q000145:classification*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D014741:Video Recording