27 January 2017 Human fixation detection model in video compressed domain based on Markov random field
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J. of Electronic Imaging, 26(1), 013008 (2017). doi: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.
© 2017 SPIE and IS&T
Yongjun Li, Yunsong Li, Weijia Liu, Jing Hu, 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 Submission: Received 11 August 2016; Accepted 19 December 2016
Submission: Received 11 August 2016; Accepted 19 December 2016
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KEYWORDS
Video

Video compression

Detection and tracking algorithms

Performance modeling

Magnetorheological finishing

Visualization

Visual process modeling

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