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CLC number: TP391

On-line Access: 2015-11-04

Received: 2015-03-18

Revision Accepted: 2015-09-14

Crosschecked: 2015-10-12

Cited: 2

Clicked: 6052

Citations:  Bibtex RefMan EndNote GB/T7714


Jia-geng Feng


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.11 P.917-929


View-invariant human action recognition via robust locally adaptive multi-view learning

Author(s):  Jia-geng Feng, Jun Xiao

Affiliation(s):  Institute of Artificial Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   fengjiageng@126.com

Key Words:  View-invariant, Action recognition, Multi-view learning, L1-norm, Local learning

Jia-geng Feng, Jun Xiao. View-invariant human action recognition via robust locally adaptive multi-view learning[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(11): 917-929.

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author="Jia-geng Feng, Jun Xiao",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T View-invariant human action recognition via robust locally adaptive multi-view learning
%A Jia-geng Feng
%A Jun Xiao
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%DOI 10.1631/FITEE.1500080

T1 - View-invariant human action recognition via robust locally adaptive multi-view learning
A1 - Jia-geng Feng
A1 - Jun Xiao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
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SP - 917
EP - 929
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500080

Human action recognition is currently one of the most active research areas in computer vision. It has been widely used in many applications, such as intelligent surveillance, perceptual interface, and content-based video retrieval. However, some extrinsic factors are barriers for the development of action recognition; e.g., human actions may be observed from arbitrary camera viewpoints in realistic scene. Thus, view-invariant analysis becomes important for action recognition algorithms, and a number of researchers have paid much attention to this issue. In this paper, we present a multi-view learning approach to recognize human actions from different views. As most existing multi-view learning algorithms often suffer from the problem of lacking data adaptiveness in the nearest neighborhood graph construction procedure, a robust locally adaptive multi-view learning algorithm based on learning multiple local L1-graphs is proposed. Moreover, an efficient iterative optimization method is proposed to solve the proposed objective function. Experiments on three public view-invariant action recognition datasets, i.e., ViHASi, IXMAS, and WVU, demonstrate data adaptiveness, effectiveness, and efficiency of our algorithm. More importantly, when the feature dimension is correctly selected (i.e., >60), the proposed algorithm stably outperforms state-of-the-art counterparts and obtains about 6% improvement in recognition accuracy on the three datasets.

This paper proposes a multi-view learning method to recognize human actions from different views. The basic motivation of the proposed method is to adaptively construct the multiple local L1-graphs. The proposed method is technically sound in general and the experimental results indicate that the proposed method is effective w.r.t. the compared baseline methods.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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