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

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

441
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...
441
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

320
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
320
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

317
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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
317
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

199
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
199
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

382
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
382
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

334
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
334

You might also read

Related Articles

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

Sort by
Same author

LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction.

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

Perceptual Quality Assessment of Low-Light Enhanced Images: A Multi-Annotated Subjective Dataset and a Multimodal Objective Method.

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

Semantic consistency-aware pseudo-temporal framework for multimodal remote sensing image segmentation.

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

Uncertainty-Driven Generative Prior Learning for Sparse Model-Guided Hyperspectral Image Fusion.

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

Second-Order Robust Iterative Pose Optimization for Fine-Grained Cross-View Localization.

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

MCIB: Multi-Modal Complementary Information Bottleneck for Hyperspectral and LiDAR Classification.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

Multi-Branch Tree-based Fusion Neural Architecture Search with Zero-Cost Screen for Multi-Modal Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: May 24, 2025

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

12.3K

CrossEI: Boosting Motion-oriented Object Tracking with An Event Camera.

Zhiwen Chen, Jinjian Wu, Weisheng Dong

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel framework for object tracking using event cameras and frame-based cameras. The method enhances tracking accuracy in dynamic scenes by adaptively fusing event-image data.

    More Related Videos

    Movement Retraining using Real-time Feedback of Performance
    08:16

    Movement Retraining using Real-time Feedback of Performance

    Published on: January 17, 2013

    13.2K
    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

    14.8K

    Related Experiment Videos

    Last Updated: May 24, 2025

    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

    12.3K
    Movement Retraining using Real-time Feedback of Performance
    08:16

    Movement Retraining using Real-time Feedback of Performance

    Published on: January 17, 2013

    13.2K
    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

    14.8K

    Area of Science:

    • Computer Vision
    • Robotics
    • Sensor Fusion

    Background:

    • Event cameras offer high temporal resolution and sensitivity, capturing motion details missed by frame-based cameras.
    • Integrating event and image data presents challenges in modality alignment and complementary cue exploitation for object tracking.
    • Existing methods struggle to effectively leverage the synergistic advantages of heterogeneous event-image sensor data in dynamic environments.

    Purpose of the Study:

    • To develop a method for aligning event and image data for enhanced object tracking.
    • To design a fusion framework that exploits the cross-complementarities between event and image modalities.
    • To improve the accuracy and robustness of object tracking in challenging dynamic scenes.

    Main Methods:

    • A motion adaptive event sampling strategy is proposed to align event-image modalities.
    • A bidirectional-enhanced fusion framework is designed, incorporating an image-guided motion estimation unit and a semantic modulation module.
    • The framework integrates aligned event-image pairs, refines event clues with explicit motion information, and modulates image features using enhanced object motion.

    Main Results:

    • The proposed method achieves state-of-the-art performance on four large benchmarks (FE108, VisEvent, FE240hz, CoeSot).
    • Significant improvements in object tracking accuracy and robustness are demonstrated, attributed to the novel sampling strategy and fusion concept.
    • The framework effectively combines the motion sensitivity of event cameras with the textural information of frame-based cameras.

    Conclusions:

    • The developed event-image fusion framework significantly enhances object tracking capabilities, particularly in dynamic scenarios.
    • The motion adaptive sampling strategy and bidirectional fusion approach are key contributions to improving tracking performance.
    • The proposed method is easily embeddable in existing tracking pipelines and trained end-to-end, offering practical applicability.