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http://dx.doi.org/10.5392/IJoC.2015.11.3.047

Object Tracking with Sparse Representation based on HOG and LBP Features  

Boragule, Abhijeet (Department of Electronics and Computer Engineering Chonnam National University)
Yeo, JungYeon (Department of Electronics and Computer Engineering Chonnam National University)
Lee, GueeSang (Department of Electronics and Computer Engineering Chonnam National University)
Publication Information
International Journal of Contents / v.11, no.3, 2015 , pp. 47-53 More about this Journal
Abstract
Visual object tracking is a fundamental problem in the field of computer vision, as it needs a proper model to account for drastic appearance changes that are caused by shape, textural, and illumination variations. In this paper, we propose a feature-based visual-object-tracking method with a sparse representation. Generally, most appearance-based models use the gray-scale pixel values of the input image, but this might be insufficient for a description of the target object under a variety of conditions. To obtain the proper information regarding the target object, the following combination of features has been exploited as a corresponding representation: First, the features of the target templates are extracted by using the HOG (histogram of gradient) and LBPs (local binary patterns); secondly, a feature-based sparsity is attained by solving the minimization problems, whereby the target object is represented by the selection of the minimum reconstruction error. The strengths of both features are exploited to enhance the overall performance of the tracker; furthermore, the proposed method is integrated with the particle-filter framework and achieves a promising result in terms of challenging tracking videos.
Keywords
Feature Extraction; Sparse Representation; Visual Object Tracking;
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