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

You might also read

Related Articles

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

Sort by

Same author

Ego4D: Around the World in 3,600 Hours of Egocentric Video.

IEEE transactions on pattern analysis and machine intelligence·2024

Same author

A domain-agnostic approach for characterization of lifelong learning systems.

Neural networks : the official journal of the International Neural Network Society·2023

Same author

Learning Spherical Convolution for 360<sup>°</sup> Recognition.

IEEE transactions on pattern analysis and machine intelligence·2021

Same author

Emergence of exploratory look-around behaviors through active observation completion.

Science robotics·2020

Same author

Learning Compressible 360 <sup>°</sup> Video Isomers.

IEEE transactions on pattern analysis and machine intelligence·2020

Same author

Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos.

IEEE transactions on pattern analysis and machine intelligence·2018

Search research articles

Related Experiment Video

Updated: Mar 8, 2026

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

Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search.

Chao Yeh Chen, Kristen Grauman

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 24, 2017

Summary

This study introduces a novel method for video activity detection, unifying categorization and localization. The approach efficiently finds activities by modeling them as a maximum-weight connected subgraph, improving speed and accuracy.

More Related Videos

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

Related Experiment Videos

Last Updated: Mar 8, 2026

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

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Activity detection in videos is crucial for understanding complex human actions.
Existing methods often struggle to efficiently unify activity categorization and precise space-time localization.
The need for faster and more accurate video analysis techniques is growing.

Purpose of the Study:

To propose an efficient and accurate approach for video activity detection.
To unify activity categorization with space-time localization within a single framework.
To develop a method that can search a broader space of space-time regions effectively.

Main Methods:

Activity detection is framed as a maximum-weight connected subgraph problem.
A binary classifier is learned offline using trimmed video exemplars.
Novel videos are decomposed into space-time nodes, weighted by feature support for the activity model.
Detection is performed by solving for the maximum-weight connected subgraph in the video's space-time graph.
An efficient branch-and-cut solution is employed for localization.

Main Results:

The proposed method achieves efficient branch-and-cut solutions for activity detection.
It enables searching a wider range of space-time region candidates.
The approach demonstrates speed and accuracy advantages over existing strategies on four datasets.
The unified approach leads to more accurate activity detection.

Conclusions:

The maximum-weight connected subgraph formulation provides an efficient and effective solution for video activity detection.
This method successfully unifies activity categorization and space-time localization.
The algorithm offers significant improvements in both speed and accuracy compared to prior methods.