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Robust Object Tracking Using Valid Fragments Selection.

Jin Zheng¹, Bo Li¹, Peng Tian²

¹Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China; State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.

Multimedia Modeling : MMM ... : Proceedings. International Conference on Multi-Media Modeling

|July 19, 2016

Summary

This summary is machine-generated.

This study introduces a novel local fragment-based visual tracking algorithm. It enhances robustness against occlusion and deformation by selecting unique, valid fragments for object tracking and template updating.

Keywords:

Fragments-based tracking Harris-SIFT filter Structured fragments Template update Valid fragment selection

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Local features enhance visual tracking robustness against occlusion and deformation.
Existing fragment-based methods often struggle with fragment weight allocation.
A need exists for more robust and adaptive object tracking techniques.

Purpose of the Study:

To propose a novel local fragment-based object tracking algorithm.
To improve tracking accuracy and robustness in challenging visual scenarios.
To develop an automatic fragment pre-selection and validation mechanism.

Main Methods:

Defining fragment discrimination and uniqueness for automatic pre-selection.
Employing a Harris-SIFT filter to identify valid, non-occluded fragments.
Utilizing fragment-based color histograms and displacement constraints for object description and tracking.
Implementing a scale-adaptive template update fusing feature similarity and valid fragments.

Main Results:

The algorithm demonstrates accurate and robust performance in challenging tracking scenarios.
The Harris-SIFT filter effectively excludes occluded or deformed fragments.
The fragment-based color histogram provides a structured object description.
The scale-adaptive template update enhances robustness to partial occlusion.

Conclusions:

The proposed local fragment-based algorithm offers significant improvements in visual tracking.
Automatic fragment selection and validation contribute to enhanced robustness.
The method is effective in handling partial occlusion, deformation, and rotation in object tracking.