background subtraction

An adaptive wavelet shrinkage based accumulative frame differencing model for motion segmentation

Motion segmentation in real-world scenes is a fundamental component in computer vision.  There exists a variety of motion recognition algorithms, each with varying degrees of accuracy and computational complexity.  The most widely used techniques, in the case of static cameras, are those based on frame difference.  Those methods have a significant weakness when it comes to detect slow moving objects.  Therefore, we introduce in this paper a novel approach that aims to improve motion segmentation by proposing an accumulative wavelet based frame differencing technique.  Moreover, in the propo

Adaptive Algorithm of Moving Objects Detection in Video Monitoring

The paper based on the analysis of existing methods of detecting moving objects offer combined detection algorithm that adapts to such destabilizing factors as: the noise of the equipment (photoelectron matrix converter, amplifier, analog-to-digital converter), change lighting scenes, change the background. The essence of the proposed algorithm is used two parallel detection methods: the method of background subtraction and frame difference method, which allows the algorithm to work effectively in cases of dynamic background.