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Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection.

Md Haidar Sharif1, Lei Jiao1, Christian W Omlin1

  • 1Department of ICT, University of Agder, 4630 Kristiansand, Norway.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces CNN-ViT-TSAN, a novel weakly supervised video anomaly event detection (WVAED) method. It effectively extracts features using CNN and Vision Transformer (ViT) models, improving anomaly detection performance.

Keywords:
Mahalanobis distanceattentionconvolutional neural network (CNN)multiple instance learning (MIL)vision transformer (ViT)weakly supervised video anomaly event detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video anomaly event detection (VAED) is crucial for smart surveillance.
  • Deep learning has advanced VAED, with weakly supervised VAED (WVAED) gaining traction.
  • Current WVAED methods rely heavily on pretrained feature extractors.

Purpose of the Study:

  • To develop a robust WVAED method that leverages diverse pretrained feature extractors.
  • To effectively capture both long-range and short-range temporal dependencies in video data.
  • To propose a generalized architecture for WVAED that integrates multiple feature extraction techniques.

Main Methods:

  • Utilized pretrained Convolutional Neural Network (CNN) models (C3D, I3D) and Vision Transformer (ViT) models (CLIP) for feature extraction.
  • Introduced a Temporal Self-Attention Network (TSAN) to model temporal dependencies.
  • Designed a Multiple Instance Learning (MIL)-based architecture, CNN-ViT-TSAN, integrating CNN/ViT features and TSAN.

Main Results:

  • The proposed CNN-ViT-TSAN architecture demonstrated effectiveness in WVAED.
  • The method successfully extracted discerning representations using combined feature extractors.
  • Experimental results on crowd datasets validated the approach's performance.

Conclusions:

  • The CNN-ViT-TSAN architecture offers a promising approach for weakly supervised video anomaly event detection.
  • Integrating diverse feature extractors and temporal attention mechanisms enhances WVAED performance.
  • The proposed method provides a generalized framework for tackling WVAED challenges.