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DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS

Abstract: 

In this work, we address human action recognition problem under viewpoint variation. The proposed model is formulated by wisely combining convolution neural network (CNN) model with principle component analysis (PCA). In this context, we pass real depth videos through a CNN model in a frame-wise manner. The view invariant features are extracted by employing convolution layers as mid-outputs and considered as 3D nonnegative tensors. The PCA algorithm is separately imposed on view-invariant high-level space of image and video groups to seek both local and holistic hidden dynamics information. To deal with noisy data and temporal misalignment, we utilize the Fourier temporal pyramid to encode temporal and obtain the final descriptors. Our proposed framework supplies a robust discriminative representation with low dimension and computational requirements. We evaluate the proposed method on two standard multiview depth video datasets. The experimental results show that our method has superior performance compared to other approaches.

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Paper Details

Authors:
Manh-Quan Bui, Viet-Hang Duong, Tzu-Chiang Tai, and Jia-Ching Wang
Submitted On:
4 October 2018 - 6:02am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Manh-Quan Bui
Paper Code:
MP.P5.4
Document Year:
2018
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Document Files

ICIP_2018_Poster.pdf

(13 downloads)

CNN_PCA_FTP_Action_Recog_Paper.pdf

(13 downloads)

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[1] Manh-Quan Bui, Viet-Hang Duong, Tzu-Chiang Tai, and Jia-Ching Wang, "DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3387. Accessed: Oct. 19, 2018.
@article{3387-18,
url = {http://sigport.org/3387},
author = {Manh-Quan Bui; Viet-Hang Duong; Tzu-Chiang Tai; and Jia-Ching Wang },
publisher = {IEEE SigPort},
title = {DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS
AU - Manh-Quan Bui; Viet-Hang Duong; Tzu-Chiang Tai; and Jia-Ching Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3387
ER -
Manh-Quan Bui, Viet-Hang Duong, Tzu-Chiang Tai, and Jia-Ching Wang. (2018). DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS. IEEE SigPort. http://sigport.org/3387
Manh-Quan Bui, Viet-Hang Duong, Tzu-Chiang Tai, and Jia-Ching Wang, 2018. DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS. Available at: http://sigport.org/3387.
Manh-Quan Bui, Viet-Hang Duong, Tzu-Chiang Tai, and Jia-Ching Wang. (2018). "DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS." Web.
1. Manh-Quan Bui, Viet-Hang Duong, Tzu-Chiang Tai, and Jia-Ching Wang. DEPTH HUMAN ACTION RECOGNITION BASED ON CONVOLUTION NUERAL NETWORKS AND PRINCIPAL COMPONENT ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3387