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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Visual Tracking via Coarse and Fine Structural Local Sparse Appearance Models.

Xu Jia, Huchuan Lu, Ming-Hsuan Yang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 23, 2016

Summary

This summary is machine-generated.

This study introduces a robust visual tracking algorithm using a local sparse appearance model. It effectively handles partial occlusion by focusing on structural information and incorporating an occlusion detection scheme for improved template updating.

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Area of Science:

Computer Vision
Machine Learning

Background:

Sparse representation is used in visual tracking but struggles with occlusions due to holistic appearance models.
Existing methods often fail to distinguish targets from backgrounds during heavy occlusion.

Purpose of the Study:

To develop a robust visual tracking algorithm that overcomes limitations of holistic sparse representation methods.
To improve tracking accuracy and occlusion handling through a local structural appearance model.

Main Methods:

A coarse and fine structural local sparse appearance model is proposed.
Sparse coding utilizes a dictionary of patches from multiple target templates.
Averaging and pooling operations exploit consistent object part appearance.
An occlusion detection scheme refines template updates by excluding occluded regions.

Main Results:

The method accurately locates targets and handles partial occlusion effectively.
Experimental results on a large benchmark dataset show favorable performance against state-of-the-art methods.

Conclusions:

The proposed local structural sparse appearance model offers a robust solution for visual tracking, especially under occlusion.
The occlusion detection scheme enhances template accuracy and overall tracking performance.