The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Evaluation of background subtraction techniques for video surveillance
Abstract
Background subtraction is one of the key techniques for automatic video analysis, especially in the domain of video surveillance. Although its importance, evaluations of recent background subtraction methods with respect to the challenges of video surveillance suffer from various shortcomings. To address this issue, we first identify the main challenges of background subtraction in the field of video surveillance. We then compare the performance of nine background subtraction methods with post-processing according to their ability to meet those challenges. Therefore, we introduce a new evaluation data set with accurate ground truth annotations and shadow masks. This enables us to provide precise in-depth evaluation of the strengths and drawbacks of background subtraction methods.
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The relationship between Precision-Recall and ROC curves
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Background subtraction techniques: a review
Piccardi, M. (2005) · 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) · doi:10.1109/icsmc.2004.1400815 · Cited by 2,083
Wallflower: principles and practice of background maintenance
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A Bayesian computer vision system for modeling human interactions
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Efficient adaptive density estimation per image pixel for the task of background subtraction
Zivkovic, Z., & van der Heijden, F. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.11.005 · Cited by 1,557
Real-time foreground–background segmentation using codebook model
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Statistical Modeling of Complex Backgrounds for Foreground Object Detection
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A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
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Traditional and recent approaches in background modeling for foreground detection: An overview
Bouwmans, T. (2014) · Computer Science Review · doi:10.1016/j.cosrev.2014.04.001 · Cited by 676
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
Li, Y., Padmanabhan, A., Zhao, P., Wang, Y., Xu, G. H., & Netravali, R. (2020) · Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication · doi:10.1145/3387514.3405874 · Cited by 269
A review of the F-measure: its history, properties, criticism, and alternatives
Christen, P., Hand, D. J., & Kirielle, N. (2024) · ACM Computing Surveys · doi:10.1145/3606367 · Cited by 236
Moving object detection based on frame difference and W4
Sengar, S. S., & Mukhopadhyay, S. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1093-8 · Cited by 78
BlockSee: Blockchain for IoT Video Surveillance in Smart Cities
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Metadata
- Authors
- Brutzer, S., Hoferlin, B., & Heidemann, G.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1937-1944
- OpenAlex
- W2069057506
- Cited by
- 622
- References
- 20
- FWCI
- 39.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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