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.
Original language | English |
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Title of host publication | AVSS2008 |
Place of Publication | USA |
Publisher | IEEE |
Pages | 41-48 |
Number of pages | 8 |
ISBN (Electronic) | 9780769533414 |
DOIs | |
Publication status | Published - 2008 |
Event | IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) - Santa Fe, New Mexico Duration: 01 Sept 2008 → 03 Sept 2008 |
Conference
Conference | IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
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Period | 01/09/08 → 03/09/08 |