Abnormal Event Detection and Signaling in Multiple Video Surveillance Scenes Using CNN

Computer vision's key duty of abnormal situation identification has applications in surveillance, anomaly monitoring, and industrial inspection. This presentation offers a summary of the methods and developments in abnormal event detection with a particular emphasis on the application of Convolutional Neural Networks (CNNs). In a variety of computer vision applications, such as object detection and picture categorization, CNNs have achieved astounding success. CNNs have been widely used for abnormal event detection because of their capacity to extract hierarchical and spatial characteristics. CNN models may learn to distinguish between normal and abnormal patterns by being trained on huge datasets of typical occurrences. This allows for efficient anomaly identification. The effectiveness of CNN-based abnormal event detection has been greatly enhanced via transfer learning. For specialized abnormal event detection applications, pre-trained CNN models, like those trained on ImageNet, offer a foundation of learnt characteristics that may be fine-tuned. The model's capacity to generalize to new datasets and previously undiscovered anomalies is improved by this transfer of information.