Human fixation detection model in video compressed domain based on Markov random field

Yongjun Li; Yunsong Li; Weijia Liu; Jing Hu; Chiru Ge

doi:10.1117/1.JEI.26.1.013008

27 January 2017 Human fixation detection model in video compressed domain based on Markov random field

Yongjun Li, Yunsong Li, Weijia Liu, Jing Hu, Chiru Ge

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 1, 013008 (January 2017). https://doi.org/10.1117/1.JEI.26.1.013008

Abstract

Recently, research on and applications of human fixation detection in video compressed domain have gained increasing attention. However, prediction accuracy and computational complexity still remain a challenge. This paper addresses the problem of compressed domain fixations detection in the videos based on residual discrete cosine transform coefficients norm (RDCN) and Markov random field (MRF). RDCN feature is directly extracted from the compressed video with partial decoding and is normalized. After spatial–temporal filtering, the normalized map [Smoothed RDCN (SRDCN) map] is taken to the MRF model, and the optimal binary label map is obtained. Based on the label map and the center saliency map, saliency enhancement and nonsaliency inhibition are done for the SRDCN map, and the final SRDCN-MRF salient map is obtained. Compared with the similar models, we enhance the available energy functions and introduce an energy function that indicates the positional information of the saliency. The procedure is advantageous for improving prediction accuracy and reducing computational complexity. The validation and comparison are made by several accuracy metrics on two ground truth datasets. Experimental results show that the proposed saliency detection model achieves superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics. Computationally, our algorithm reduces 26% more computational complexity with comparison to similar algorithms.

Citation Download Citation

Yongjun Li, Yunsong Li, Weijia Liu, Jing Hu, and Chiru Ge "Human fixation detection model in video compressed domain based on Markov random field," Journal of Electronic Imaging 26(1), 013008 (27 January 2017). https://doi.org/10.1117/1.JEI.26.1.013008

Received: 11 August 2016; Accepted: 19 December 2016; Published: 27 January 2017

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

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
17 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Video

Video compression

Detection and tracking algorithms

Performance modeling

Magnetorheological finishing

Visualization

Visual process modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years