- Title
- On stable dynamic background generation technique using Gaussian mixture models for robust object detection
- Creator
- Haque, Mohammad; Murshed, Manzur; Paul, Manoranjan
- Date
- 2008
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/38147
- Identifier
- vital:6271
- Identifier
-
https://doi.org/10.1109/AVSS.2008.12
- Identifier
- ISBN:978-0-7695-3422-0
- Abstract
- Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences.
- Publisher
- Santa Fe, NM IEEE
- Relation
- 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance p. 41-48
- Rights
- This metadata is freely available under a CCO license
- Subject
- Gaussian processes; Image motion analysis; Image sequences; Object detection; Surveillance; 0801 Artificial Intelligence and Image Processing
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