An intelligent Surveillance System for Detecting Abnormal Behaviors on Campus using YOLO and CNN-LSTM Networks

Like many other surveillance systems, security and safety are mainly what concern people these days. It can be used in two methods, watching threats in real-time or searching for an event that already happened. Surveillance systems can be defined in many different ways like violence or any abnormal behaviour detection. In this paper, we propose a surveillance system for detecting abnormal behaviours on campus using deep learning models. It also includes how YOLO, CNN, and LSTM are used to apply concepts like feature extraction, object detection, action detection and identification. YOLO algorithm was used in the smoking detection and the playing cards achieving the accuracy of 90% and 93.8% respectively. As for the CNN-LSTM model, we achieved an accuracy of 93.5%.