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 Experiment Video

Updated: Jun 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

PADS: a probabilistic activity detection framework for video data.

Massimiliano Albanese1, Rama Chellappa, Naresh Cuntoor

  • 1Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA. albanese@umiacs.umd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 27, 2010
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Fixed Action Patterns01:06

Fixed Action Patterns

A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.

You might also read

Related Articles

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

Sort by
Same author

Deep Learning for the Detection of Corneal Perforation on Anterior-Segment Optical Coherence Tomography in Microbial Keratitis.

Bioengineering (Basel, Switzerland)·2026
Same author

Deep Learning for Detection of Corneal Perforation on Anterior Segment Optical Coherence Tomography in Microbial Keratitis.

medRxiv : the preprint server for health sciences·2026
Same author

MediCARE: Medical Collaborative Agents REasoning over Interpretable Heterogeneous Graphs.

Artificial intelligence in medicine·2026
Same author

Recovering Pulse Waves From Video Using Deep Unrolling and Deep Equilibrium Models.

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

Innovation in geriatrics: what this series means for care.

Innovation in aging·2025
Same author

A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.

European radiology·2025
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces a new language and framework for video activity detection. The developed system accurately identifies complex activities, outperforming existing methods for real-time and post-playout analysis.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Identifying activities in videos is increasingly important.
  • Existing methods may lack flexibility or efficiency for complex activity recognition.

Purpose of the Study:

  • To develop a flexible probabilistic framework for specifying and detecting video activities.
  • To introduce efficient algorithms for real-time and post-hoc activity detection.

Main Methods:

  • Developed the Probabilistic Activity Description Language (PADL) for activity specification.
  • Created a probabilistic framework for activity detection.
  • Implemented OffPad (post-hoc) and OnPad (real-time) algorithms within the Probabilistic Activity Detection System (PADS).

Related Experiment Videos

Last Updated: Jun 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

Main Results:

  • PADS effectively detects specified activities in video sequences.
  • OffPad identifies minimal video segments containing target activities.
  • OnPad computes probabilities of ongoing activities during video playback, even for incomplete actions.

Conclusions:

  • The proposed framework and algorithms offer superior performance, especially for complex activity definitions.
  • PADS demonstrates significant improvements over four existing approaches in video activity detection.
  • The system provides a robust solution for both real-time and offline video analysis.