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

Frames: Problem Solving I

Frames: Problem Solving I

Consider a jib crane with an external load suspended from the pulley. The dimensions of the crane members are shown in the figure. A systematic analysis of the frame structure is required to determine the reaction forces at the pin joints, assuming that the pulleys are frictionless.

Frames: Problem Solving II

Frames: Problem Solving II

Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

You might also read

Related Articles

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

Sort by

Same author

Signal and Supply: Phytohormonal networks regulating source sink dynamics and assimilate partitioning.

Plant science : an international journal of experimental plant biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Key frame extraction algorithm for surveillance videos using an evolutionary approach.

Manjusha Rajan¹, Latha Parameswaran²

¹Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, India, 641112. r_manjusha@cb.amrita.edu.

Scientific Reports

|January 3, 2025

Summary

This study introduces an efficient Key Frame Extraction (KFE) algorithm using an interactive Genetic Algorithm (GA) for generic video summarization. The new GA-based KFE method outperforms existing techniques, offering superior efficiency and performance.

Keywords:

Elitism Evolutionary computation Genetic algorithm Key frame extraction

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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

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

Published on: June 15, 2020

Related Experiment Videos

Last Updated: Jul 9, 2026

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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

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

Published on: June 15, 2020

Area of Science:

Computer Science
Artificial Intelligence
Multimedia Processing

Background:

Video data is rapidly increasing, necessitating efficient summarization techniques.
Key Frame Extraction (KFE) is vital for video summarization, compression, and analysis.
Existing KFE methods lack versatility for generic video applications.

Purpose of the Study:

To develop an efficient and versatile Key Frame Extraction (KFE) approach for generic videos.
To leverage evolutionary algorithms, specifically a Genetic Algorithm (GA), for optimal KFE.
To enhance KFE performance through an interactive GA with a well-designed Fitness Function and elitism-based survivor selection.

Main Methods:

An interactive Genetic Algorithm (GA) with a custom Fitness Function and elitism-based survivor selection was developed.
The proposed KFE algorithm was evaluated on diverse datasets including VSUMM, SumMe, Mall, user-generated, surveillance, and web-sourced videos.
Computational complexity was compared against Differential Evolution (DE) and Deep Learning (DL) approaches.

Main Results:

The proposed KFE approach demonstrated adherence to benchmark data and captured additional significant frames.
The algorithm showed superior efficiency and performance compared to existing Differential Evolution (DE) and Deep Learning (DL) models.
Quantitative and qualitative evaluations confirmed the effectiveness of the developed KFE technique.

Conclusions:

The developed interactive GA-based KFE algorithm offers a more versatile solution for generic video summarization.
This approach provides a computationally efficient and high-performing alternative to current KFE methods.
The study highlights the potential of evolutionary algorithms in advancing video processing technologies.