Open AccessAdaptive shadow detection using global texture and sampling deduction
Author(s) -
Jiang Ke,
Li Aihua,
Cui Zhigao,
Wang Tao,
Su Yanzhao
Publication year - 2013
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0106
Subject(s) - shadow (psychology) , artificial intelligence , computer vision , computer science , estimator , object detection , pixel , adaptive sampling , interference (communication) , pattern recognition (psychology) , mathematics , statistics , psychology , computer network , channel (broadcasting) , monte carlo method , psychotherapist
An adaptive shadow detection algorithm is proposed to eliminate interference on object detection from the shadow. The algorithm uses three components in YUV colour space to identify shadow pixels from the candidate foreground. An adaptive threshold estimator is designed to improve shadow detection accuracy and adaptive capacity in various lighting conditions. This estimator uses edge detection method to obtain global texture, as well statistical calculations to obtain the thresholds. Algorithm has the characteristic of low complexity and little restraint; hence it is suitable for real time‐moving shadow detection in various lighting conditions. Experiment results show that this algorithm can obtain a high detection accuracy and the time‐assume is greatly shortened compared with other algorithms with similar accuracy.