Digital Image Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
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Detecting moving objects from video frames is one of the key techniques in computer vision. Background subtraction is a common way to detect moving objects at present. A new background subtraction algorithm is proposed in this paper. The algorithm describes backgrounds by a combination of hue and improved local binary pattern (LBP) texture and adopts the idea of Gaussian mixture model that uses multiple modes to represent background. In order to reduce matching complexity and satisfy real-time, the LBP texture feature vectors are simplified. Experiments show that the proposed algorithm can satisfy real-time in common resolution videos, can remove effectively the effect of shadow and can detect moving objects more accurately than others.