Real-Time Anomaly Detection and Classification from Surveillance Cameras Using Deep Neural Network

With increasing security threats, anomaly detection and classification are highly recommended work nowadays. Anomaly detection and classification from surveillance videos are more complex tasks due to the prevalence of anomalous activity. There are still some exceptional problems that require advanced approaches. Deep learning has recently made it possible to detect and classify anomalies in a critical way. In this paper, we have proposed a fine-tuned ResNet-50 model to learn anomalous patterns by exploiting 14 types of anomalous images. In our approach, we have first augmented the image data before passing it to the model. Instead of a fully connected layer, we have added an average pooling layer, dropout layer, flatten, dense layer, and dense layer followed by an activation function (softmax). We also introduce a new dataset that consists of 10483 real-world anomalous images, with 14 realistic anomalies, including abuse, fire, road accident, robbery, suicide attempt, etc. Increasing the classification performance, such baselines demonstrate that it is incredibly tough for our dataset and opens up more opportunities for future work¹. In terms of accuracy, our proposed model can acquire 100% accuracy for anomaly detection, and for anomaly classification, on average, it acquires 79.69% accuracy with a computational cost of 61.45 milliseconds per frame.