Probabilistic static foreground elimination for background subtraction

Background subtraction is generally used to detect moving objects, because it has low complexity, and it is easy to implement. However, the detection error increases when the background is changing. Therefore, adaptive background subtraction is applied to overcome this problem, and it continuously r...

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Bibliographic Details
Published in:The imaging science journal 2019-10, Vol.67 (7), p.385-395
Main Authors: Rungruangbaiyok, Sunthorn, Duangsoithong, Rakkrit, Chetpattananondh, Kanadit
Format: Article
Language:English
Subjects:
Adaptive background subtraction
foreground elimination
learning rate
moving object detection
probability
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Summary:Background subtraction is generally used to detect moving objects, because it has low complexity, and it is easy to implement. However, the detection error increases when the background is changing. Therefore, adaptive background subtraction is applied to overcome this problem, and it continuously requires updating the background with a fixed learning rate. The learning rate should be tuned for a consistently evolving background. This paper proposes the Probabilistic Static Foreground Elimination for Background Subtraction (PSFE) algorithm. It consisted of two parameters: the number of frames for static foreground elimination, and the probability of changes in background pixels. These two parameters can tune the learning rate and update background for better detection. The average results of the detection error rate from Wallflower datasets were tested with PSFE and well-known method. They demonstrated that PSFE provides moving object detection with minimum detection error (5.95%), especially in Camouflage, Moved object, and Light switch dataset.
ISSN:1368-2199
1743-131X
DOI:10.1080/13682199.2019.1672849