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Development of game theoretic hypergraph based autoencoder scheme for multiple objects tracking and anomaly detection

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This study introduces a unified framework for anomaly detection in surveillance, integrating Game-Theoretic Hypergraph Matching (GTHG) with a Convolutional Autoencoder (CAE). The novel approach enhances both accuracy and efficiency in identifying suspicious activities during object tracking.

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AutoencoderGame-theoryHypergraphRobust featuresVideo anomaly detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Anomaly detection in surveillance is vital for safety and security.
  • Graph and hypergraph (HG)-based methods aid object tracking for anomaly detection.
  • Existing methods struggle to balance accuracy and efficiency in simultaneous tracking and detection.

Purpose of the Study:

  • To propose a unified framework for anomaly detection in surveillance footage.
  • To integrate Game-Theoretic Hypergraph Matching (GTHG) with a Convolutional Autoencoder (CAE).
  • To improve both detection accuracy and computational performance by combining structural consistency and appearance reconstruction.

Main Methods:

  • Developed a unified framework integrating GTHG and CAE.
  • Combined structural consistency and appearance reconstruction for anomaly detection.
  • Tested the method on benchmarked video datasets, analyzing frame-to-frame matching.

Main Results:

  • Achieved high Area Under the Curve (AUC) scores: 88.7% (UCSD Ped1), 91.2% (UCSD Ped2), and 86.6% (CUHK Avenue).
  • Demonstrated superior performance compared to existing anomaly detection models.
  • Validated the effectiveness of the integrated approach in improving accuracy and efficiency.

Conclusions:

  • The proposed GTHG and CAE integrated framework offers a significant advancement in anomaly detection.
  • This unified approach effectively addresses the limitations of separate tracking and detection methods.
  • The framework provides a robust solution for enhancing surveillance safety and security standards.