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: Apr 12, 2026

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

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

Published on: December 25, 2017

8.8K

Stampede detection and crowd analysis using CNN-LSTM and farneback optical flow.

Geetanjali Bhola1, Sumit Srivastava2, Hith Rahil Nidhan3

  • 1Faculty of Technology, University of Delhi, Delhi, NCT of Delhi, 110007, India.

Scientific Reports
|April 10, 2026
PubMed
Summary
This summary is machine-generated.

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

Design and development of a model for tennis elbow injury prediction and prevention using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) approaches.

BMC musculoskeletal disorders·2025
Same author

Multimodal biometric authentication system leveraging optimally trained ensemble classifier using feature-level fusion.

Technology and health care : official journal of the European Society for Engineering and Medicine·2025
Same author

Pharmacoinformatics in identifying therapeutically important chemical species from Ayurvedic formulations employed in treating COVID-19 patients.

Journal of Ayurveda and integrative medicine·2025
Same author

In vitro anti-inflammatory and in silico anti-viral assessment of phytoconstituents in polyherbal Ayurvedic formulation 'Arogyamrita Kwath'.

Journal of Ayurveda and integrative medicine·2025
Same author

Ashwagandha (Withania somnifera) and Shunthi (Zingiber officinale) in mild and moderate COVID-19: An open-label randomized controlled exploratory trial.

Complementary therapies in medicine·2023
Same author

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects.

Multimedia tools and applications·2023

This study introduces an automated system for detecting crowd stampedes and classifying crowd risk levels. The novel framework achieves 99.75% accuracy, enhancing public safety at crowded events.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Safety

Background:

  • Stampede incidents in crowded environments pose significant public safety risks, leading to casualties and disruptions.
  • Existing methods for crowd monitoring often lack the granularity needed for effective early warning systems.

Purpose of the Study:

  • To develop and validate a novel, data-driven framework for automated stampede detection and crowd risk classification.
  • To provide a more granular assessment of crowd states beyond traditional binary models.

Main Methods:

  • Integration of Farneback optical-flow computation with a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture.
  • Utilization of combined UCSD Anomaly detection and Agoraset datasets for comprehensive crowd behavior and density analysis.
Keywords:
Abnormal crowd behaviorCNN–LSTMCrowd analysisDeep learningOptical flowSpatio-temporalStampede detection

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.7K

Related Experiment Videos

Last Updated: Apr 12, 2026

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

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

Published on: December 25, 2017

8.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.7K
  • Training and evaluation on 10,000 annotated frames with preprocessing and augmentation for robustness.

    • Main Results:

    • Achieved a high accuracy of 99.75% in classifying crowd states into four risk levels: normal, moderate, dense, and risky.
    • Demonstrated robustness under domain shift conditions through cross-dataset evaluation on the UMN benchmark.
    • The system offers a granular assessment of crowd risk, outperforming traditional binary models.

    Conclusions:

    • The proposed framework shows strong potential for real-time deployment in public event management and emergency response systems.
    • Current limitations include computational latency and challenges in detecting stampedes in ultra-dense, occluded crowd scenarios.
    • Further research is needed to address computational efficiency and performance in complex, occluded environments.