An adaptive background model initialization algorithm with objects moving at different depths
Chia-Chih Chen,Jake K. Aggarwal +1 more
- 12 Dec 2008
- pp 2664-2667
TL;DR: This paper proposes an algorithm which is adaptive to the input sequence and is able to equalize the uneven effect caused by different object depths, which is successfully tested on complex indoor and outdoor scenes with promising results.
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Abstract: Background subtraction is an essential element in most object tracking and video surveillance systems. The success of this low-level processing step is highly dependent on the quality of the background model maintained. Gutchess et al. [4] proposed a novel background initialization algorithm that utilizes local optical flow information to locate the stable interval (of intensity values) which is most likely to display background. However, it is found that the accuracy of the computed background is rather sensitive to the parameters used. In addition, their algorithm is not able to handle the scenario where objects are moving at different depths. In this paper, we propose an algorithm which is adaptive to the input sequence and is able to equalize the uneven effect caused by different object depths. Our algorithm is successfully tested on complex indoor and outdoor scenes with promising results.
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- 07 Sep 2015
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•Posted Content
Towards Benchmarking Scene Background Initialization
TL;DR: In this paper, a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.
99
Scene background initialization: A taxonomy
TL;DR: A taxonomy study for background initialization is proposed, providing the basis for a fair and easy comparison of existing and future methods, on a common dataset of groundtruthed sequences, with a common set of metrics, and based on reproducible results.
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References
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
- 23 Jun 1999
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Non-parametric Model for Background Subtraction
Ahmed Elgammal,David Harwood,Larry S. Davis +2 more
- 26 Jun 2000
TL;DR: A novel non-parametric background model that can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes is presented.
Wallflower: principles and practice of background maintenance
Kentaro Toyama,John Krumm,Barry Brumitt,Brian R. Meyers +3 more
- 01 Sep 1999
TL;DR: This work develops Wallflower, a three-component system for background maintenance that is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur.
A background model initialization algorithm for video surveillance
D. Gutchess,M. Trajkovics,Eric Cohen-Solal,Damian M. Lyons,Anil K. Jain +4 more
- 07 Jul 2001
TL;DR: A new algorithm is presented for the purpose of background model initialization, which takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene.
292
Active Surveillance Using Dynamic Background Subtraction
Kenneth M. Dawson-Howe
- 31 Aug 1996
TL;DR: The system has been tested on a di cult real-world scene and image sequences from both cameras are presented and the potential of the system as an `intelligent' security device and the power of the dynamic background subtraction & correlation mechanism are clearly demonstrated.
23
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