Discriminative object tracking with subspace representation

Visual object tracking is a core research area in the field of pattern recognition and computer vision. It becomes one of the most significant tasks in computer vision application. But tracking of a visual object is not an easy task as it is always restricted by appearance change, illumination, occlusion and so on. Object tracking based on principal component analysis (PCA) is one of the most effective tracking methods as it can handle the different challenging problems of the tracking algorithm. But in this PCA-based tracking method, the background pixels are also included in the subspace representation of the target object, and so this method cannot overcome all the problems of tracking. In this work, a robust visual object tracking method is proposed by introducing sparse discriminative classifier (SDC) feature selection in PCA subspace representation. The SDC method is utilized to extract the target object from the template image target by removing the background pixels which is unnecessary for tracking task without much computational complexity. The PCA algorithm adequately represents a presentation model of the target object and account of occlusion with trivial template. Qualitative and quantitative analysis of different diverse videos shows that the newly proposed method outperforms the other existing state-of-the-art tracking algorithm.

Automated summary

The importance of visual object tracking and the PCA-based tracking method were studied, and a new visual object tracking method incorporating sparse discriminative classifier feature selection was proposed, outperforming other existing tracking algorithms.