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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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LTGS-Net: Local Temporal and Global Spatial Network for Weakly Supervised Video Anomaly Detection.

Minghao Li1, Xiaohan Wang1, Haofei Wang2

  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

This study introduces the Local Temporal and Global Spatial Network (LTGS) for weakly supervised video anomaly detection. The LTGS method improves detection accuracy by effectively capturing spatio-temporal dependencies, outperforming existing algorithms.

Keywords:
LTGSdynamic labelsspatio-temporal fusionvideo anomaly detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Weakly supervised video anomaly detection is crucial for intelligent surveillance but challenged by sparse events and labeling costs.
  • Current methods often process spatial and temporal features independently, hindering the capture of complex interdependencies and impacting detection performance.
  • The need for robust and accurate anomaly detection in videos necessitates novel approaches that effectively integrate spatio-temporal information.

Purpose of the Study:

  • To propose a novel network architecture, the Local Temporal and Global Spatial Network (LTGS), for weakly supervised video anomaly detection.
  • To enhance the capture of spatio-temporal dependencies by processing local temporal and global spatial features collaboratively.
  • To improve model robustness and accuracy through a dynamic label updating strategy.

Main Methods:

  • Developed the Local Temporal and Global Spatial Network (LTGS) incorporating clip-level temporal relation and video-level spatial feature modules.
  • Employed joint training of these modules to create a specialized feature encoder for video anomaly detection.
  • Implemented a dynamic label updating strategy to refine clip-level annotations and optimize model performance.

Main Results:

  • The LTGS method achieved high performance on benchmark datasets, with an AUC of 96.69% on ShanghaiTech and 82.33% on UCF-Crime.
  • Demonstrated superior performance compared to various state-of-the-art algorithms in video anomaly detection tasks.
  • Validated the effectiveness of the proposed architecture and dynamic label updating strategy through extensive experiments.

Conclusions:

  • The LTGS network effectively addresses the limitations of independent spatio-temporal feature processing in weakly supervised video anomaly detection.
  • The proposed method significantly improves detection accuracy and robustness, offering a promising solution for intelligent surveillance systems.
  • The study highlights the importance of integrated spatio-temporal analysis and adaptive labeling for advancing video anomaly detection research.