Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning

Gang-Joon Yoon

doi:10.3390/s18103513

Return

Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning

2018-10-18

Gang-Joon Yoon

Hyeong Jae Hwang

Sang Min Yoon *

DOI：10.3390/s18103513

Original

Save

View PDF

Abstract

En 中文

Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences.

Keywords:

visual object tracking structured sparse PCA

appearance model

online learning

structured visual dictionary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.