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Proceedings Paper

Extremally similar regions sifting for moving object segmentation in infrared videos
Author(s): Hua Ye; Guanzheng Tan
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Paper Abstract

It is difficult to study human actions on visual cognition as individual differences and dynamic environment causes a large number of variables. Adaptive mining the connectivity of moving human contour in infrared images based on regions can improve detecting moving object performance. We propose adaptive motion detection algorithm based on layering frequency sifting and maximally similar regions measuring in this letter, to overcome difficulties to sample moving human contour from dynamic background. First using frequency sifting layer by layer of input infrared images by Bidimensional Empirical Mode Decomposition (BEMD) representations, the original images were layered into bidimensional intrinsic mode functions (BIMFs). Thus connected edge information is remained on BIIMFs while smoothing data is filtered. Then detected connected regions using Maximally Stable Extremal Regions(MSERs) representation amongst BIMFs and the original image. Since being similarity amongst those connected regions of those images, which includes the moving human contour. At last measured similar MSERs regions hierarchically. The maximal similar connected regions segmented is candidate moving object contours. The experiment results on several open infrared videos show that the proposed algorithm improves credibility and simplicity, superior to other unsupervised measures.

Paper Details

Date Published: 24 October 2017
PDF: 6 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046208 (24 October 2017); doi: 10.1117/12.2281500
Show Author Affiliations
Hua Ye, Central South Univ. (China)
Hunan Univ. of Arts and Sciences (China)
Guanzheng Tan, Central South Univ. (China)

Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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