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