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Structured fragment-based object tracking using discrimination, uniqueness, and validity selection.

Jin Zheng¹, Bo Li¹, Ming Xin¹

¹Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China.

Multimedia Systems

|February 12, 2020

Summary

This summary is machine-generated.

This study introduces a novel local fragment-based visual tracking algorithm. It enhances robustness by selecting only unique, discriminative, and valid fragments for object tracking, improving accuracy in challenging conditions.

Keywords:

Discriminative and unique feature Fragment-based tracking Harris-SIFT filter Structured fragment Template update

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Area of Science:

Computer Vision
Machine Learning

Background:

Local features are crucial for robust visual tracking against occlusion and deformation.
Existing fragment-based methods often use all fragments, potentially diluting discriminative power.

Purpose of the Study:

To propose a novel local fragment-based object tracking algorithm.
To enhance tracking robustness by selectively using discriminative and valid local fragments.

Main Methods:

Defined discrimination and uniqueness metrics for local fragments.
Implemented an automatic pre-selection mechanism for fragments.
Utilized a Harris-SIFT filter to select valid fragments, excluding occluded or deformed ones.
Constructed a structured object description using selected fragments.
Performed tracking by combining fragment displacement, similarity, and spatial constraints.
Updated the object template using feature similarity and structural consistency.

Main Results:

The proposed algorithm achieved reliable tracking on the OTB 2013 benchmark dataset.
Demonstrated robustness against significant appearance changes, partial occlusion, and similar disturbances.

Conclusions:

Selective fragment selection significantly improves visual tracking performance.
The proposed method offers a more robust and accurate approach to object tracking in complex scenarios.