Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.7K
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...
4.7K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.1K
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...
5.1K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

1.0K
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Inoculation fermentation improves the nutritional quality and flavor profile of Chinese traditional fermented okara (Meitauza): a comparison with a commercial benchmark.

Frontiers in microbiology·2026
Same author

Nursery pressures in megacities: Interplay of anthropogenic disturbance and natural drivers in shaping ichthyoplankton community dynamics in urbanized coasts.

Journal of environmental management·2026
Same author

Hierarchical Quaternized Cuprous Oxide: A Single-Component Hybrid for Synergistic Antibacterial Surfaces and Accelerated WoundHealing.

ACS applied bio materials·2026
Same author

A nanopore sequencing scheme for MiniHap markers and its application in kinship analysis.

Forensic science international. Genetics·2026
Same author

Fate of Ultraviolet Absorbents in a Marine Ecosystem: Bioaccumulation, Tissue-Specific Distribution, and Trophic Transfer in the Yellow Sea.

Environmental science & technology·2026
Same author

Bioinspired antibacterial microrobots derived from mammalian cells for biofilm disruption.

Chemical communications (Cambridge, England)·2026

Related Experiment Video

Updated: Mar 6, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

381

Robust Scale Adaptive Tracking by Combining Correlation Filters with Sequential Monte Carlo.

Junkai Ma1,2,3, Haibo Luo4,5, Bin Hui6,7

  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. junkai.ma@hotmail.com.

Sensors (Basel, Switzerland)
|March 10, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a robust object tracking algorithm that adapts to target scale changes and handles occlusions effectively. The novel approach improves tracking accuracy in complex computer vision scenarios.

Keywords:
correlation filterocclusionscale estimationsequential Monte Carlo frameworktarget tracking

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.9K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

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

Published on: June 15, 2020

13.5K

Related Experiment Videos

Last Updated: Mar 6, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

381
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.9K
Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

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

Published on: June 15, 2020

13.5K

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Object tracking is crucial for computer vision but faces challenges like occlusion and scale variation.
  • Existing trackers struggle with complex scenarios, necessitating more robust solutions.

Purpose of the Study:

  • To develop a robust scale-adaptive object tracking algorithm.
  • To improve tracking performance in the presence of occlusion and scale variations.

Main Methods:

  • Combines sequential Monte Carlo for scale prediction with correlation filters for location determination.
  • Analyzes response map's peak-to-sidelobe rate (PSR) to detect occlusion.
  • Implements a strict template update strategy and a retained scheme for occluded targets.
  • Integrates features to enhance overall robustness.

Main Results:

  • The proposed tracker demonstrates superior performance compared to state-of-the-art methods.
  • Achieved higher distance precision and overlap precision on the TB-50 dataset.
  • Effectively handles target scale variation and occlusion.

Conclusions:

  • The developed scale-adaptive tracker offers improved robustness and efficiency.
  • It provides a reliable solution for object tracking in challenging computer vision applications.