Exploring Deep Learning Techniques for Abnormality Detection: A Comparative Analysis on UCF Crime Dataset.
Keywords:
Video Surveillance, Abnormality, Deep Learning, VGG16 CNN, VGG16 BiLSTM, RNN, Slow Fast Network, UCF Crime, DenseNet121.Abstract
Video surveillance systems have been used significantly to enhance the security of a variety of places that are both private and public. The automatic detection of abnormalities in video surveillance is an intriguing research topic. Despite the recent development of multimedia-based anomaly detection algorithms, video surveillance is still inadequate to detect unexpected events such as illegal acts and crimes. In this study, comparison-based deep learning models are utilised to construct an automated framework that can detect anomalies in videos. To identify abnormalities these four models, VGG16 CNN, VGG16 Bi-LSTM, Slow Fast Network, and DenseNet121 have been used. The proposed approaches have been evaluated using the UCF-Crime dataset. This study distinguishes between usual and unusual events, showing that comparison-based models were capable of classifying each abnormal event. Slow Fast Network obtained 99% accuracy on the UCF-Crime dataset, VGG16 CNN achieved 98% accuracy, VGG16 Bi-LSTM scored 96% accuracy, and DenseNet121 achieved 70% accuracy. Furthermore, the proposed models outperformed comparable deep-learning models in terms of performance accuracy. Our comparison analysis paper describes:
- How the comparison-based approach is effective for understanding the deep learning model for Abnormal detection using the UCF crime dataset?
- This paper highlights that Slow Fast Network and VGG16 CNN Deep Learning Models performed better than the other existing models and also helped to classify the Abnormality Detection from the UCF Crime Dataset.
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