Adaptive Change Detection for Real-Time Surveillance Applications
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thresholding for Change Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Image Subtraction for Real Time Moving Object Extraction
CGIV '04 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Ontological inference for image and video analysis
Machine Vision and Applications
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a knowledge-based framework for video analysis which systematically exploits relationship among analysis stages. A set of step-by-step feedback paths controls feedback generation and reception between consecutive analysis stages. An analysis ontology, which includes occurrences in the scene from high to very low semantic level, controls iterative decisions on every stage. As a result, both overall and intermediate analysis results are improved. This paper presents the framework and focuses on its application to foreground objects extraction. Experimental results show that the framework provides a richer low-level representation of the scene and improved short-term change detection and foreground detection masks.