| Autor: |
Damera, Vijay Kumar, Vatambeti, Ramesh, Mekala, M. S., Pani, Alok Kumar, Manjunath, Chinthakunta |
| Předmět: |
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| Zdroj: |
International Journal of Safety & Security Engineering; Feb2023, Vol. 13 Issue 1, p51-58, 8p |
| Abstrakt: |
Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low- and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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