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

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...

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...

Absolute Motion Analysis- General Plane Motion

Absolute Motion Analysis- General Plane Motion

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 drone...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Implicit neural representation-based method for metal-induced beam hardening artifact reduction in X-ray CT imaging.

Medical physics·2025

Same author

Deep Learning-Based Automated Measurement of Cervical Length in Transvaginal Ultrasound Images of Pregnant Women.

IEEE journal of biomedical and health informatics·2024

Same author

Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification.

Medical image analysis·2024

Same author

A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography.

PloS one·2022

Same author

Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Physics in medicine and biology·2022

Same author

A fidelity-embedded learning for metal artifact reduction in dental CBCT.

Medical physics·2022

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Environmentally robust motion detection for video surveillance.

Hyenkyun Woo, Yoon Mo Jung, Jeong-Gyoo Kim

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

|October 19, 2010

Summary

This study introduces a new motion detection model for video surveillance systems. The variational energy-based method offers robust detection without manual sensitivity tuning, reducing false alarms and detection failures.

Related Experiment Videos

Last Updated: Jun 8, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Area of Science:

Computer Vision
Image Processing
Surveillance Technology

Background:

Traditional video surveillance systems rely on manually set motion detection sensitivity levels.
Preselected sensitivity in Closed Circuit Television (CCTV) and Digital Video Recorder (DVR) systems often leads to false alarms and missed detections across diverse environmental conditions.
Existing algorithms struggle with varying illumination and noise, necessitating manual parameter adjustments.

Purpose of the Study:

To develop an automated motion detection model that eliminates the need for manual sensitivity tuning.
To enhance the robustness of motion detection algorithms against environmental variations such as illumination changes and noise.
To provide a reliable and efficient motion detection solution for embedded surveillance systems.

Main Methods:

A novel motion detection model is proposed, utilizing variational energy principles.
The model's mathematical structure is rigorously analyzed.
Extensive experiments were conducted under various environmental conditions to validate the model's performance.

Main Results:

The proposed model demonstrates robust motion detection capabilities across different illumination levels and noise conditions.
Experimental results confirm the model's effectiveness without requiring any manual parameter tuning.
The system achieved a significant reduction in false alarms and detection failures compared to traditional methods.

Conclusions:

The variational energy-based motion detection model offers a superior alternative to conventional systems.
Its parameter-free nature and robustness make it highly suitable for real-world surveillance applications.
The model's compact and efficient design allows for implementation in resource-constrained embedded systems.