Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization

Ka Ki Ng; Edward J. Delp

doi:10.1117/12.872610

31 January 2011 Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization

Ka Ki Ng, Edward J. Delp

Proceedings Volume 7882, Visual Information Processing and Communication II; 78820I (2011) https://doi.org/10.1117/12.872610
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

In this paper we present a new method for object tracking initialization using background subtraction. We propose an effective scheme for updating a background model adaptively in dynamic scenes. Unlike the traditional methods that use the same "learning rate" for the entire frame or sequence, our method assigns a learning rate for each pixel according to two parameters. The first parameter depends on the difference between the pixel intensities of the background model and the current frame. The second parameter depends on the duration of the pixel being classified as a background pixel. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model.

Citation Download Citation

Ka Ki Ng and Edward J. Delp "Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization", Proc. SPIE 7882, Visual Information Processing and Communication II, 78820I (31 January 2011); https://doi.org/10.1117/12.872610

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