CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Introduction to the Special Section on Empirical Evaluation of Computer Vision Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Characterization in Computer Vision
CAIP '93 Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns
Frame-Rate Omnidirectional Surveillance & Tracking of Camouflaged and Occluded Targets
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Adaptive key frame extraction for video summarization using an aggregation mechanism
Journal of Visual Communication and Image Representation
Continuity in Wireless Video Security System-Based Physical Security Services
Wireless Personal Communications: An International Journal
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Intelligent video surveillance (IVS) technology is on the cusp of moving from early adopters to general acceptance in several markets such as security and business intelligence. This transition has been made possible by embedding computer vision technologies directly into video devices such as cameras, encoders, routers, DVRs, NVRs, and other video management and storage hardware. For this technology to be successful, it is crucial that IVS systems can be deployed easily, without requiring computer vision expertise to customize them for every installation; and that the systems work robustly in a wide range of environments. One of the key enablers to achieve this goal is proper testing. This paper discusses some of the major challenges involved and provides a case study for addressing the problem. One of the key concepts is utilizing fuzzy evaluation to handle boundary conditions.