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...
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| Vydáno v: | Intelligent Systems and Computer Vision (Online) s. 1 - 4 |
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| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
08.05.2024
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| ISSN: | 2768-0754 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Lahraichi, Mohammed Housni, Khalid Berroukham, Abdelhafid |
| Author_xml | – sequence: 1 givenname: Abdelhafid surname: Berroukham fullname: Berroukham, Abdelhafid email: a.berroukham@gmail.com organization: Ibne Tofail University,Faculty of Science,Department of Computer Science,Kenitra,Morocco – sequence: 2 givenname: Khalid surname: Housni fullname: Housni, Khalid email: housni.khalid@uit.ac.ma organization: Ibne Tofail University,Faculty of Science,Department of Computer Science,Kenitra,Morocco – sequence: 3 givenname: Mohammed surname: Lahraichi fullname: Lahraichi, Mohammed email: lahraichi.mohamed@gmail.com organization: Ibne Tofail University,Faculty of Science,Department of Computer Science,Kenitra,Morocco |
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| Snippet | This research investigates the application of a novel LSTM Convolutional Autoencoder (LSTM-CAE) framework for video anomaly detection, utilizing the UCSD... |
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| SubjectTerms | Anomaly detection Autoencoder Deep Learning Ensemble learning Event detection Intelligent systems LSTM Security Surveillance Transfer learning |
| Title | Abnormal Event Detection in Videos using LSTM Convolutional Autoencoder |
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