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Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...

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Transformer and Adaptive Threshold Sliding Window for Improving Violence Detection in Videos.

Fernando J Rendón-Segador1, Juan A Álvarez-García1, Luis M Soria-Morillo1

  • 1Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, 41012 Sevilla, Spain.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This study introduces CrimeNet, a Vision Transformer model for video violence detection, enhanced by an adaptive threshold sliding window. This approach significantly improves cross-dataset violence detection accuracy, overcoming generalization challenges.

Keywords:
adaptive thresholddeep learningsliding windowtransformerviolence detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Video-based violence detection is crucial for public safety and content moderation.
  • Existing models like Vision Transformers (ViT) show promise but struggle with cross-dataset generalization.
  • Challenges include performance degradation when applied to unseen datasets.

Purpose of the Study:

  • To develop a robust video violence detection system.
  • To address the generalization limitations of current deep learning models.
  • To enhance the accuracy and reliability of violence event detection across diverse datasets.

Main Methods:

  • Developed CrimeNet, a Vision Transformer (ViT) model incorporating structured neural learning and adversarial regularization.
  • Implemented an adaptive threshold sliding window model, also based on Transformer architecture, for post-processing.
  • Evaluated performance on multiple benchmark datasets including XD-Violence, UCF-Crime, and RWF-2000.

Main Results:

  • CrimeNet achieved high performance (AUC ROC up to 99%, AUC PR up to 100%) on individual datasets.
  • Cross-dataset generalization issues caused a 20-30% performance drop.
  • The adaptive threshold sliding window improved cross-dataset accuracy by 10-15%.

Conclusions:

  • The combined approach of CrimeNet and the adaptive sliding window model significantly enhances video violence detection accuracy, especially in cross-dataset scenarios.
  • This method effectively mitigates generalization problems inherent in deep learning models.
  • Future work should focus on further improving generalization, handling data imbalance, and exploring multimodal approaches.