Silhouettes based human action recognition by Procrustes analysis and Fisher vector encoding

Jiaxin Cai; Xin Tang; Ranxu Zhong

doi:10.1117/12.2506632

29 October 2018 Silhouettes based human action recognition by Procrustes analysis and Fisher vector encoding

Jiaxin Cai, Xin Tang, Ranxu Zhong

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 1083612 (2018) https://doi.org/10.1117/12.2506632
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

Recently, human action recognition in videos has attracted much attention. This paper proposed a framework for human action recognition based on procrustes analysis and Fisher vector encoding. First, we apply a pose based feature extracted from silhouette image by employing Procrustes analysis and local preserving projection. It can preserve the discriminative shape information and local manifold structure of human pose and is invariant to translation, rotation and scaling. After the pose feature is extracted, a recognition framework based on Fisher vector encoding and multi-class supporting vector machine is employed for classifying the human action. Experimental results on benchmarks demonstrate the effectiveness of the proposed method.

Citation Download Citation

Jiaxin Cai, Xin Tang, and Ranxu Zhong "Silhouettes based human action recognition by Procrustes analysis and Fisher vector encoding", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083612 (29 October 2018); https://doi.org/10.1117/12.2506632

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available