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

Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

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 instrumental in...

Relative Motion Analysis - Acceleration

Relative Motion Analysis - Acceleration

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...

Relative Motion Analysis using Rotating Axes - Acceleration

Relative Motion Analysis using Rotating Axes - Acceleration

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...

Relative Motion Analysis - Velocity

Relative Motion Analysis - Velocity

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...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

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...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by

Same author

Impact of Gyroscope Integration, Sensor Placement, and Activity Granularity on Human Activity Recognition Performance.

Sensors (Basel, Switzerland)·2026

Same author

Inertial Sensor-Based Motion Calibration for 1-DoF Joint Angle Estimation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Design and Characterization of a Wearable Inertial Measurement Unit.

Sensors (Basel, Switzerland)·2024

Same author

Estimation of Ground Contact Time with Inertial Sensors from the Upper Arm and the Upper Back.

Sensors (Basel, Switzerland)·2023

Same author

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models.

Sensors (Basel, Switzerland)·2023

Same author

Editorial: Biometric monitoring of emotions and behaviors.

Frontiers in neurorobotics·2022

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Direct Linear Transformation for the Measurement of In-Situ Peripheral Nerve Strain During Stretching

Direct Linear Transformation for the Measurement of In-Situ Peripheral Nerve Strain During Stretching

Published on: January 12, 2024

Video alignment for change detection.

Ferran Diego¹, Daniel Ponsa, Joan Serrat

¹Computer Vision Center and Computer Science Department, Edifici O, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Spain. fdiego@cvc.uab.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 2, 2010

Summary

This study presents a new method for video alignment using GPS and image data, enabling synchronization and spatial registration for independently moving cameras. This approach enhances applications like vehicle detection and surveillance by improving temporal and spatial correspondence.

Related Experiment Videos

Last Updated: Jun 6, 2026

Direct Linear Transformation for the Measurement of In-Situ Peripheral Nerve Strain During Stretching

Direct Linear Transformation for the Measurement of In-Situ Peripheral Nerve Strain During Stretching

Published on: January 12, 2024

Area of Science:

Computer Vision
Robotics
Signal Processing

Background:

Video alignment is crucial for tasks requiring temporal and spatial correspondence between video sequences.
Existing methods often rely on restrictive assumptions like fixed cameras or simultaneous acquisition, limiting their applicability.
Independent camera motion and varying acquisition times pose significant challenges for accurate video alignment.

Purpose of the Study:

To develop a general method for aligning video sequences captured by independently moving cameras with similar trajectories.
To overcome limitations of previous approaches by fusing image intensity and GPS information.
To enable robust video synchronization and spatial registration without prior knowledge of camera trajectories or linear time correspondence.

Main Methods:

Formulating video synchronization as a Maximum A Posteriori (MAP) inference problem within a Bayesian network.
Integrating complementary data from image intensity and GPS sensors for enhanced alignment accuracy.
Developing a novel approach that fuses multi-modal sensor data for robust video sequence alignment.

Main Results:

Successfully aligned video sequences recorded by vehicles traversing the same route at different times and under varied road conditions.
Demonstrated the effectiveness of the proposed method in handling independently moving cameras and non-linear temporal correspondences.
Validated the complementary nature of image intensity and GPS data for achieving accurate video synchronization and spatial registration.

Conclusions:

The proposed Bayesian network approach effectively addresses the general problem of video alignment for independently moving cameras.
Fusion of image intensity and GPS data provides a robust solution for video synchronization and spatial registration.
The developed video alignment method has practical applications in advanced driver-assistance systems (ADAS) and surveillance, particularly for change detection.