Search research articles

Related Concept Videos

Calibration Curves: Correlation Coefficient

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

Implementation of Q learning and deep Q network for controlling a self balancing robot model.

Robotics and biomimetics·2019

Same journal

Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power-assist robotic system.

Robotics and biomimetics·2018

Same journal

PID, BFO-optimized PID, and PD-FLC control of a two-wheeled machine with two-direction handling mechanism: a comparative study.

Robotics and biomimetics·2018

Same journal

Systematic engineering design helps creating new soft machines.

Robotics and biomimetics·2018

Same journal

Hybrid control combined with a voluntary biosignal to control a prosthetic hand.

Robotics and biomimetics·2018

Same journal

A multi-jointed underactuated robot hand with fluid-driven stretchable tubes.

Robotics and biomimetics·2018

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Feb 18, 2026

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

A comparison study of adaptive scale estimation in correlation filter-based visual tracking methods.

Z L Wang¹, B G Cai¹

¹Beijing Jiaotong University, Beijing, 100044 China.

Robotics and Biomimetics

|November 21, 2017

Summary

This summary is machine-generated.

Discriminative correlation filters (DCF) excel in real-time visual tracking. This study reviews adaptive scale estimation methods for DCF, crucial for long-term tracking accuracy.

More Related Videos

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Related Experiment Videos

Last Updated: Feb 18, 2026

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Area of Science:

Computer Vision
Machine Learning

Background:

Discriminative Correlation Filter (DCF) methods are popular for visual tracking due to efficiency and performance.
Current DCF methods primarily focus on translation estimation, neglecting accurate scale estimation.

Purpose of the Study:

To review and analyze adaptive scale estimation approaches in DCF-based visual tracking.
To provide a better understanding of scale estimation challenges within DCF frameworks.

Main Methods:

Introduction to the principles of DCF-based visual tracking.
Summarization of various adaptive scale estimation techniques used in DCF.
Experimental comparison and performance analysis of different scale estimation methods.

Main Results:

DCF methods offer high computational efficiency suitable for real-time applications.
Accurate scale estimation remains a significant challenge in long-term DCF tracking.
Experimental analysis provides insights into the performance of different scale estimation strategies.

Conclusions:

Understanding scale estimation is vital for improving DCF visual tracking.
Future work may integrate scale estimation strategies with other factors like appearance variation to enhance DCF performance.