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Related Concept Videos

Collisions in Multiple Dimensions: Introduction

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...

Collisions in Multiple Dimensions: Problem Solving

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...

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Related Experiment Video

Updated: May 5, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

IDG-ViolenceNet: A Video Violence Detection Model Integrating Identity-Aware Graphs and 3D-CNN.

Hong Huang¹, Qingping Jiang¹

¹School of Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin 644000, China.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

This study introduces IDG-ViolenceNet, a novel dual-stream model for video violence detection. It significantly improves accuracy in complex scenarios by integrating identity-aware graphs with 3D-CNNs for enhanced surveillance.

Keywords:

3D-CNN identity-aware modeling multi-object tracking public safety spatiotemporal GNN violence detection

Related Experiment Videos

Last Updated: May 5, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Intelligent surveillance and public safety rely on effective video violence detection.
Existing methods struggle with modeling complex multi-person interactions in videos.

Purpose of the Study:

To propose IDG-ViolenceNet, a dual-stream model for advanced video violence detection.
To enhance the modeling of complex human interactions for improved public safety.

Main Methods:

Developed a dual-stream model integrating identity-aware spatiotemporal graphs with 3D-CNNs.
Utilized YOLOv11 for precise person detection and identity tracking.
Constructed dynamic spatiotemporal graphs encoding spatial, temporal, and identity information.

Main Results:

Achieved high accuracies: 97.5% (Hockey Fight), 99.5% (Movies Fight), and 89.4% (RWF-2000).
Significantly outperformed existing state-of-the-art methods in violence detection.
Ablation studies confirmed the effectiveness of individual model components.

Conclusions:

IDG-ViolenceNet offers a robust and accurate solution for video violence detection.
The integration of identity-aware graphs and 3D-CNNs effectively captures complex interactions.
The model shows strong potential for real-world intelligent surveillance applications.