Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Image and Video Anomaly Detection Using AI Based Deepanomaly Detectors


Affiliations
1 Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, India
2 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, India
3 Department of Mechanical Engineering, Pravara Rural Engineering College, India
4 Department of Information Technology, M. Kumarasamy College of Engineering, India
5 Department of Computer Science and Information Technology, Jazan University,, Saudi Arabia
     

   Subscribe/Renew Journal


In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method.

Keywords

Anomaly Detection, Deep Learning, Image Analysis, Computer Vision, Video Processing
Subscription Login to verify subscription
User
Notifications
Font Size

  • Y. Chang, H. Sui and J. Yuan, “Video Anomaly Detection with Spatio-Temporal Dissociation”, Pattern Recognition, Vol. 122, pp. 1-13, 2022.
  • Y. Hao, X. Wang and X. Gao, “Spatiotemporal Consistency-Enhanced Network for Video Anomaly Detection”, Pattern Recognition, Vol. 121, pp. 1-12, 2022.
  • A. Berroukham and I. Boulfrifi, “Deep Learning-Based Methods for Anomaly Detection in Video Surveillance: A Review”, Bulletin of Electrical Engineering and Informatics, Vol. 12, No. 1, pp. 314-327, 2023.
  • Y. Liu, J. Liu, J. Lin, M. Zhao and L. Song, “Appearance-Motion United Auto-Encoder Framework for Video Anomaly Detection”, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69, No. 5, pp. 2498-2502, 2022.
  • Y. Liu, J. Liu, J. Lin, M. Zhao and L. Song, “Amp-Net: Appearance-Motion Prototype Network Assisted Automatic Video Anomaly Detection System”, IEEE Transactions on Industrial Informatics, Vol. 87, No. 2, pp. 1-13, 2023.
  • T. Ganokratanaa and N. Sebe, “Video Anomaly Detection using Deep Residual-Spatiotemporal Translation Network”, Pattern Recognition Letters, Vol. 155, pp. 143-150, 2022.
  • G. Wang, Y. Wang, J. Qin, D. Zhang and D. Huang, “Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles”, Proceedings of European Conference on Computer Vision, pp. 494-511, 2022.
  • W. Liu, S. Shan and X. Chen, “Diversity-Measurable Anomaly Detection”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12147-12156, 2023.
  • R. Raja and D.K. Saini, “Analysis of Anomaly Detection in Surveillance Video: Recent Trends and Future Vision”, Multimedia Tools and Applications, Vol. 82, No. 8, pp. 12635-12651, 2023.
  • J. Fioresi and M. Shah, “Ted-Spad: Temporal Distinctiveness for Self-Supervised Privacy-Preservation for Video Anomaly Detection”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13598-13609, 2023.
  • Q. Zhang, G. Feng and H. Wu, “Surveillance Video Anomaly Detection via Non-Local U-Net Frame Prediction”, Multimedia Tools and Applications, Vol. 81, No. 19, pp. 27073-27088, 2022.
  • D.R. Patrikar and M.R. Parate, “Anomaly Detection using Edge Computing in Video Surveillance System”, International Journal of Multimedia Information Retrieval, Vol. 11, No. 2, pp. 85-110, 2022.
  • A. Barbalau, J. Dueholm, B. Ramachandra and M. Shah, “SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection”, Computer Vision and Image Understanding, Vol. 229, pp. 1-16, 2023.
  • W. Wang, F. Chang and C. Liu, “Mutuality-Oriented Reconstruction and Prediction Hybrid Network for Video Anomaly Detection”, Signal, Image and Video Processing, Vol. 16, No. 7, pp. 1747-1754, 2022.

Abstract Views: 58

PDF Views: 1




  • Image and Video Anomaly Detection Using AI Based Deepanomaly Detectors

Abstract Views: 58  |  PDF Views: 1

Authors

M. Elavarasi
Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, India
R. Pramodhini
Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, India
M. Deshmukh Deepak
Department of Mechanical Engineering, Pravara Rural Engineering College, India
R. Mekala
Department of Information Technology, M. Kumarasamy College of Engineering, India
Chamandeep Kaur
Department of Computer Science and Information Technology, Jazan University,, Saudi Arabia

Abstract


In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method.

Keywords


Anomaly Detection, Deep Learning, Image Analysis, Computer Vision, Video Processing

References