Learning Motion Detectors by Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Local histogram of figure/ground segmentations for dynamic background subtraction
EURASIP Journal on Advances in Signal Processing
Robust moving object detection against fast illumination change
Computer Vision and Image Understanding
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In the traditional mixture of Gaussians background model, the generating process of each pixel is modeled as a mixture of Gaussians over color. Unfortunately, this model performs poorly when the background consists of dynamic textures such as trees waving in the wind and rippling water. To address this deficiency, researchers have recently looked to more complex and/or less compact representations of the background process. We propose a generalization of the MoG model that handles dynamic textures. In the context of background modeling, we achieve better, more accurate segmentations than the competing methods, using a model whose complexity grows with the underlying complexity of the scene (as any good model should), rather than the amount of time required to observe all aspects of the texture.