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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.9K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Multi-Frame Temporal Integration for 3-D Shape Measurement of Freely Falling Small Objects Using a High-Speed Camera Array.

Sensors (Basel, Switzerland)·2026
Same author

Current-Aware Temporal Fusion with Input-Adaptive Heterogeneous Mixture-of-Experts for Video Deblurring.

Sensors (Basel, Switzerland)·2026
Same author

HFR-Video-Based Stereo Correspondence Using High Synchronous Short-Term Velocities.

Sensors (Basel, Switzerland)·2023
Same author

An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection.

Sensors (Basel, Switzerland)·2023
Same author

Origami Folding by Multifingered Hands with Motion Primitives.

Cyborg and bionic systems (Washington, D.C.)·2022
Same author

Development of an Active High-Speed 3-D Vision System.

Sensors (Basel, Switzerland)·2019
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

Related Experiment Video

Updated: Jun 22, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.9K

High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range

Tianyi Zhang1, Kohei Shimasaki2, Idaku Ishii2

  • 1Namiki Laboratory, Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a high-speed active vision system (AVS) for tracking small objects in magnified scenes. It utilizes a continuous autofocus (C-AF) method and a high-frame-rate (HFR) pipeline to achieve precise object tracking.

Keywords:
autofocushigh-speed visionliquid lensobject tracking

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K
Video-rate Scanning Confocal Microscopy and Microendoscopy
14:10

Video-rate Scanning Confocal Microscopy and Microendoscopy

Published on: October 20, 2011

27.9K

Related Experiment Videos

Last Updated: Jun 22, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.9K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K
Video-rate Scanning Confocal Microscopy and Microendoscopy
14:10

Video-rate Scanning Confocal Microscopy and Microendoscopy

Published on: October 20, 2011

27.9K

Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Optical Engineering

Background:

  • Active vision systems (AVSs) are crucial for high-resolution imaging but struggle with tracking small objects in high-magnification scenarios due to limited depth of field (DoF) and field of view (FoV).
  • Existing AVS limitations necessitate advancements for effective magnified object tracking.

Purpose of the Study:

  • To introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach and a high-frame-rate (HFR) tracking pipeline.
  • To overcome the challenges of tracking small objects in high-magnification scenes by improving focus and frame rate capabilities.

Main Methods:

  • Developed a C-AF approach using a 500 fps camera and a liquid lens for a 50 Hz focal sweep, capturing 10 images per sweep to select the sharpest image at 50 fps.
  • Integrated depth-from-focus (DFF) for dynamic focal sweep adjustment and utilized all 500 fps images for tracking.
  • Implemented an HFR tracking pipeline combining deep learning object detection, K-means color clustering, and color filtering for 500 fps frame-by-frame tracking.

Main Results:

  • Achieved stable, well-focused images at 50 fps using the C-AF method.
  • Enabled 500 fps frame-by-frame object tracking by leveraging all captured frames.
  • Demonstrated the effectiveness of the proposed C-AF and HFR tracking for magnified object tracking.

Conclusions:

  • The novel high-speed AVS effectively addresses challenges in magnified object tracking.
  • The integrated C-AF and HFR tracking pipeline significantly enhances tracking precision and speed.
  • The system shows advanced capabilities for applications requiring high-resolution, high-speed tracking of small objects.