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http://dx.doi.org/10.9717/kmms.2015.18.12.1432

Moving Shadow Detection using Deep Learning and Markov Random Field  

Lee, Jong Taek (Regional Industry IT Convergence Research Section, Daegu-Gyeongbuk Research Center, IT Convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute (ETRI))
Kang, Hyunwoo (Regional Industry IT Convergence Research Section, Daegu-Gyeongbuk Research Center, IT Convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute (ETRI))
Lim, Kil-Taek (Regional Industry IT Convergence Research Section, Daegu-Gyeongbuk Research Center, IT Convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute (ETRI))
Publication Information
Journal of Korea Multimedia Society / v.18, no.12, 2015 , pp. 1432-1438 More about this Journal
Abstract
We present a methodology to detect moving shadows in video sequences, which is considered as a challenging and critical problem in the most visual surveillance systems since 1980s. While most previous moving shadow detection methods used hand-crafted features such as chromaticity, physical properties, geometry, or combination thereof, our method can automatically learn features to classify whether image segments are shadow or foreground by using a deep learning architecture. Furthermore, applying Markov Random Field enables our system to refine our shadow detection results to improve its performance. Our algorithm is applied to five different challenging datasets of moving shadow detection, and its performance is comparable to that of state-of-the-art approaches.
Keywords
Moving Shadow Detection; Convolutional Neural Network; Markov Random Field; Surveillance System; Object Detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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