Search research 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

Related Concept Videos

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

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...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

You might also read

Related Articles

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

Sort by

Same author

AnyDesign: Versatile area fashion editing via mask-free diffusion.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Continual Instruction Tuning for Large Multimodal Models.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Adversarial discriminant attack on text-to-image diffusion models.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.

International journal of computer assisted radiology and surgery·2025

Same author

Hierarchical Contrastive Learning for Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2025

Same author

BrainCLIP: Brain Representation via CLIP for Generic Natural Visual Stimulus Decoding.

IEEE transactions on medical imaging·2025

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 10, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking.

Bin Yu^1,2, Ming Tang², Guibo Zhu^1,2

¹School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

This summary is machine-generated.

This study introduces DCOM, a novel visual tracking method using distribution calibration and overlap maximization. It enhances target estimation accuracy and robustness by leveraging large-scale datasets for reliable reference information.

Keywords:

bounding box estimation distribution calibration overlap maximization visual tracking

More Related Videos

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Related Experiment Videos

Last Updated: Oct 10, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Area of Science:

Computer Vision
Machine Learning

Background:

Overlap maximization improves visual tracking but is limited by target reference information.
Current methods struggle with robustness and accuracy due to insufficient reference data.

Purpose of the Study:

Introduce DCOM, a novel bounding box estimation method for visual tracking.
Enhance target estimation robustness and accuracy by utilizing distribution calibration and overlap maximization.

Main Methods:

DCOM assumes Gaussian distribution for modulation vector dimensions, enabling borrowing from similar targets in large datasets.
Employs an updating strategy for the modulation vector to adapt to target variations.
Integrates with existing networks without finetuning or extra parameters.

Main Results:

Achieves state-of-the-art performance on GOT-10k, LaSOT, and NfS benchmarks.
Demonstrates effectiveness and efficiency with a running speed of approximately 40 FPS.
Provides sufficient and reliable reference information through calibrated distributions for improved target estimation.

Conclusions:

DCOM offers a robust and accurate solution for visual tracking.
The method is efficient and adaptable to target object variations.
DCOM represents a significant advancement in bounding box estimation for visual tracking.