Illumination independent change detection for real world image sequences
Computer Vision, Graphics, and Image Processing
Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Change detection using joint intensity histogram
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Local Properties of Binary Images in Two Dimensions
IEEE Transactions on Computers
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Histogram thresholding using fuzzy and rough measures of association error
IEEE Transactions on Image Processing
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical change detection with moments under time-varying illumination
IEEE Transactions on Image Processing
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Crowd counting and segmentation in visual surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive human motion analysis and prediction
Pattern Recognition
Journal of Visual Communication and Image Representation
Steering kernel-based video moving objects detection with local background texture dictionaries
Computers and Electrical Engineering
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The extraction of moving objects from their background is a challenging task in visual surveillance. As a single threshold often fails to resolve ambiguities and correctly segment the object, in this paper, we propose a new method that uses three thresholds to accurately classify pixels as foreground or background. These thresholds are adaptively determined by considering the distributions of differences between the input and background images and are used to generate three boundary sets. These boundary sets are then merged to produce a final boundary set that represents the boundaries of themoving objects. The merging step proceeds by first identifying boundary segment pairs that are significantly inconsistent. Then, for each inconsistent boundary segment pair, its associated curvature, edge response, and shadow index are used as criteria to evaluate the probable location of the true boundary. The resulting boundary is finally refined by estimating the width of the halo-like boundary and referring to the foreground edge map. Experimental results show that the proposed method consistently performs well under different illumination conditions, including indoor, outdoor, moderate, sunny, rainy, and dim cases. By comparing with a ground truth in each case, both the classification error rate and the displacement error indicate an accurate detection, which show substantial improvement in comparison with other existing methods.