Modified foreground segmentation for object tracking using wavelets in a tensor framework

Abstract

Subspace-based techniques have become important in behaviour analysis, appearance modelling and tracking. Various vector and tensor subspace learning techniques are already known that perform their operations in offline as well as in an online manner. In this work, we have improved upon a tensor-based subspace learning by using fourth-order decomposition and wavelets so as to have an advanced adaptive algorithm for robust and efficient background modelling and tracking in coloured video sequences. The proposed algorithm known as fourth-order incremental tensor subspace learning algorithm uses the spatio-colour-temporal information by adaptive online update of the means and the eigen basis for each unfolding matrix using tensor decomposition to fourth-order image tensors. The proposed method employs the wavelet transformation to an optimum decomposition level in order to reduce the computational complexity by working on the approximate counterpart of the original scenes and also reduces noise in the given scene. Our tracking method is an unscented particle filter that utilises appearance knowledge and estimates the new state of the intended object. Various experiments have been performed to demonstrate the promising and convincing nature of the proposed method and the method works better than existing methods.

Keywords:

• adaptive visual tracking
• background modelling
• subspace update
• wavelet