Anomaly Detection in Surveillance Camera
K. Gopalakrishnan, K. Kavitha, S. Manisha, S.G. Kanish, D. Divya Dharshini, C.L. Dharshan · 2024
In high security locations, anomaly detection in surveillance systems is essential for maintaining public safety. This work proposes an object detection approach utilizing model renowned for its real-time performance for anomaly detection. The system perceives objects and trails actions by using YOLOv5 where it notes down the odd behaviors as perhaps in security video where it abnormal. A dataset which contains of several surveillance synopsis is used to train and evaluate the model. YOLOv5 has such an object detection capability with a custom made revelation framework which grants the system to differentiate between abnormal and legal activities in real-time. An emphasis on prudent processing speed is done to provide scalability in practical applications and the high authenticity abnormality detection is determined by experimental findings.