Real Time Abnormal Event Detection in Pedestrain Pathways Using Deep Learning

In recent days, pedestrian surveillance paths are utilized by many people, and the safety issue has become a point of concern in crowded areas. In general, most surveillance systems rely on people watching the cameras, which is inefficient. Although watching in general, it is difficult to spot abnormal events when many events are happening at the same time. Because of this problem, the idea of automating the process of spotting abnormal events was investigated in this project. This project involves the detection of abnormal events by using deep learning methodologies. A CNN model with VGG16 is utilized to analyze images captured from surveillance cameras. Transfer learning is applied to make the model learn faster and better with fewer images. The system is developed using the Django framework, where people can upload images and get predictions. From the predictions, it is clear that the system is more efficient than watching in general and saves human