Performance evaluation in visual surveillance using the F-measure
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
An object-based comparative methodology for motion detection based on the F-Measure
Computer Vision and Image Understanding
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
Evaluating descriptors performances for object tracking on natural video data
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
A software pipeline for 3D animation generation using mocap data and commercial shape models
Proceedings of the ACM International Conference on Image and Video Retrieval
Modeling and assessing quality of information in multisensor multimedia monitoring systems
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
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This paper introduces a methodology for evaluating the operational range of a video surveillance system in terms of robustness and reliability. We propose the generation of semi and full-synthetic video sequences under controlled variation of selected parameters. This data provides the necessary ground truth information for evaluating the motion detection and tracking systems. In addition, we propose several error metrics for quantitative evaluation.