Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Statistical change detection with moments under time-varying illumination
IEEE Transactions on Image Processing
Waterfront surveillance and trackability
Machine Vision and Applications
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
Computer vision technologies for monitoring system in tele-physiotherapy
Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
HOG-based descriptors on rotation invariant human detection
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Recognition of complex human behaviors in pool environment using foreground silhouette
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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One difficult challenge in autonomous video surveillance is on handling highly dynamic backgrounds. This difficulty is compounded if foreground objects of interest are partially hidden by specular reflections or glare. In this paper, we provide numerous insights into the technical difficulties faced in developing an automated video surveillance system within a hostile environment: an outdoor public swimming pool. For robust detection performance, we focused on two central aspects: i) effective modeling of the dynamic outdoor aquatic background with rapid illumination changes, splashes and random spatial movements of background elements, owing to the movement of water ripples; and ii) enhancing the visibility of swimmers that are partially hidden by specular reflections. Several innovations have been introduced from scratch in this paper. The first is the development of a scheme that models the background as regions of dynamic homogeneous processes. This model facilitates the implementation of an efficient spatial searching scheme for background subtraction that could exploit long-range spatial dependencies between pixels. The second is the implementation of a spatio-temporal filtering scheme that enhances the detection of swimmers that are partially hidden by specular reflections of artificial nighttime lighting, serving as a pre-processing module to foreground detection for nighttime operation. These various algorithms have been tightly integrated under a unified framework and demonstrated on a busy Olympic-sized outdoor public pool.