Abnormal Event Detection in Videos using LSTM Convolutional Autoencoder

This research investigates the application of a novel LSTM Convolutional Autoencoder (LSTM-CAE) framework for video anomaly detection, utilizing the UCSD Anomaly Detection Dataset. Traditional methods in video anomaly detection often face challenges in capturing both temporal and spatial dependencie...

Full description

Saved in:
Bibliographic Details
Published in:Intelligent Systems and Computer Vision (Online) pp. 1 - 4
Main Authors: Berroukham, Abdelhafid, Housni, Khalid, Lahraichi, Mohammed
Format: Conference Proceeding
Language:English
Published: IEEE 08.05.2024
Subjects:
Anomaly detection
Autoencoder
Deep Learning
Ensemble learning
Event detection
Intelligent systems
LSTM
Security
Surveillance
Transfer learning
ISSN:2768-0754
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This research investigates the application of a novel LSTM Convolutional Autoencoder (LSTM-CAE) framework for video anomaly detection, utilizing the UCSD Anomaly Detection Dataset. Traditional methods in video anomaly detection often face challenges in capturing both temporal and spatial dependencies within dynamic scenes. The proposed LSTM-CAE integrates Long Short-Term Memory networks and Convolutional Autoencoders to overcome these challenges, demonstrating superior performance compared to conventional techniques. Despite challenges in model interpretability and hyperparameter tuning, the findings emphasize the efficacy of the proposed approach in enhancing precision and adaptability in video anomaly detection. The implications of this research extend to diverse applications in security and surveillance. Future work may explore ensemble methods, attention mechanisms, and transfer learning for further refinement and generalizability of the LSTM-CAE framework. Overall, this study contributes to the advancement of video anomaly detection methodologies, providing a foundation for continued exploration and application of deep learning approaches in this domain.
ISSN:2768-0754
DOI:10.1109/ISCV60512.2024.10620098