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Robust Visual Tracking with Reliable Object Information and Kalman Filter.

Hang Chen¹, Weiguo Zhang¹, Danghui Yan¹

¹Automation College, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)

|February 2, 2021

Summary

This summary is machine-generated.

This study introduces a robust visual tracking framework using a Kalman filter (KF) to improve object tracking accuracy. The method enhances reliability by analyzing target information confidence and employing novel online training for better performance in challenging scenarios.

Keywords:

Kalman filter correlation filter reliable information visual object tracking

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Visual tracking performance is heavily influenced by object information accuracy.
Challenging scenarios like occlusion and appearance changes degrade traditional tracking methods.
Existing correlation filter methods suffer from noise and loss of target information due to linear interpolation updates.

Purpose of the Study:

To propose a novel robust visual tracking framework.
To enhance tracking reliability by analyzing target information confidence.
To improve robustness against challenging tracking scenarios.

Main Methods:

Developed a framework integrating a Kalman filter (KF) with correlation filters.
Implemented a reliability analysis to determine the necessity of online model updates.
Introduced a new online training method to preserve reliable target information.

Main Results:

The proposed method demonstrates improved robustness in visual tracking.
Experiments show comparable or superior performance against state-of-the-art trackers.
The framework effectively handles challenging scenarios like occlusion and clutter.

Conclusions:

The novel framework significantly enhances visual tracking robustness and reliability.
Integrating Kalman filter and adaptive online training improves performance.
The method offers a promising solution for accurate object tracking in complex environments.