Space–time spectral model for object detection in dynamic textured background

Background models are used for object detection in many computer vision algorithms. In this article, we propose a novel background modeling method based on frequency for spatially varying and time repetitive textured background. The local Fourier transform is applied to construct a pixel-wise representation of local frequency components. We apply our method for object detection in moving background conditions. Experimental results of our frequency-based background model are evaluated both qualitatively and quantitatively.

► A novel frequency based background modeling method is proposed.
► Local Fourier transform is used for feature extraction.
► Spectral background analysis is performed.
► Foreground objects are detected using the approach.
► The results are compared with the results of mixture of Gaussian method.