Detecting the Unseen: A Study of CNNs for Real Time Abnormal Event Detection


Authors : Zainab Khan; Varsha K; Uzaira Sahar; Vaibhavi M.G; Velvizhi Ramya R

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/bde82hwm

Scribd : https://tinyurl.com/46m5jmy7

DOI : https://doi.org/10.5281/zenodo.14557313

Abstract : Abnormal event detection is a critical task in various domains, including surveillance, healthcare, and industrial monitoring. This paper explores the application of Convolutional Neural Network (CNNs) for detecting abnormal events in dynamic environments. By leveraging CNNs’ ability to extract spatial and temporal features, we primarily aim to enhance the accuracy and efficiency of anomaly detection. To validate our approach, we employed a comparative analysis of supervised and unsupervised learning techniques. Extensive experimentation on our datasets demonstrated that CNNs consistently outperformed traditional methods in identifying anomalies with higher precision and recall rates. The results highlight the potential of CNNs as a robust solution for abnormal event detection, effectively balancing computational efficiency and detection accuracy. Our findings highlight the importance of integrating domain-specific insights and using advanced architectures to address all the challenges in anomaly detection.

Keywords : Event Detection, CNN, K-means, Logistic Regression, Supervised, Unsupervised, Model, NLP, Image Recognition.

References :

  1. Abnormal Event Detection in Crwoded Places by Cong, Yang: Yuan, Junsong: Liu, Ji
  2. Abnormal Event Detection in Crwoded Places by Cong, Yang: Yuan, Junsong: Liu, Ji
  3. Shangai Tech University, Tencent AI Lab; Margin Learning Embedded Prediction for Anomaly Detection by Wen Liu, Weixin Luo, Zhenxgin Li, Pein Zhao, Shenghua Gao
  4. Shangai Tech University, Tencent AI Lab; Margin Learning Embedded Prediction for Anomaly Detection by Wen Liu, Weixin Luo, Zhenxgin Li, Pein Zhao, Shenghua Gao
  5. Unsupervised clustering Methods for identifying rare events by Witcha Chimphlee, Abdul Hanan, Mohd Noor, Siripon Chimphlee, Surat
  6. Unsupervised clustering Methods for identifying rare events by Witcha Chimphlee, Abdul Hanan, Mohd Noor, Siripon Chimphlee, Surat
  7. Event Detection in Videos using Spatiotemporal autoencoder by Yong Shean Chong.
  8. CNN and 1-Class Event Classifier in 8th International Conference Imaging by Samir Buoindour, Mazen Hittawe, Sandy, Hichem Snoussi.
  9. CNN and 1-Class Event Classifier in 8th International Conference Imaging by Samir Buoindour, Mazen Hittawe, Sandy, Hichem Snoussi.
  10. CNN and 1-Class Event Classifier in 8th International Conference Imaging by Samir Buoindour, Mazen Hittawe, Sandy, Hichem Snoussi.
  11. Image Architecture of Convolutional Neural Networks by K.Eswaran in journal – Research Gate.
  12. On identifying leaves: A comparison of CNN with Classical ML methods by Abbas Hedjazi, Ikram Kourban, Yakup Genc.
  13. On identifying leaves: A comparison of CNN with Classical ML methods by Abbas Hedjazi, Ikram Kourban, Yakup Genc.

Abnormal event detection is a critical task in various domains, including surveillance, healthcare, and industrial monitoring. This paper explores the application of Convolutional Neural Network (CNNs) for detecting abnormal events in dynamic environments. By leveraging CNNs’ ability to extract spatial and temporal features, we primarily aim to enhance the accuracy and efficiency of anomaly detection. To validate our approach, we employed a comparative analysis of supervised and unsupervised learning techniques. Extensive experimentation on our datasets demonstrated that CNNs consistently outperformed traditional methods in identifying anomalies with higher precision and recall rates. The results highlight the potential of CNNs as a robust solution for abnormal event detection, effectively balancing computational efficiency and detection accuracy. Our findings highlight the importance of integrating domain-specific insights and using advanced architectures to address all the challenges in anomaly detection.

Keywords : Event Detection, CNN, K-means, Logistic Regression, Supervised, Unsupervised, Model, NLP, Image Recognition.

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