Suspicious Human Activity Recognition and Alarming System
S Leela · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: Personal safety and social stability are seriously threatened by the violent activities. A variety of methods have been tried to curb the violent activities which includes installing of surveillance systems. It will be of great significance if the surveillance systems can automatically detect violent activities and give warning or alert signals. The whole system can be implemented with a sequence of procedures. Firstly, the system has to identify the presence of human beings in a video frame. Then, the frames which are predicted to contain violent activities has to be extracted. The irrelevant frames are to be dropped at this stage. Finally, the trained model detects violent behaviour and these frames are separately saved as images. The images along with other necessary details such as time and location is sent as an alert to the concerned authority. The proposed method is a deep learning based automatic detection approach that uses Convolutional Neural Network to detect violence present in a video. But, the disadvantage of using just CNN is that, it requires a lot of time for computation and is less accurate. Hence, a pre-trained model, Mobile Net, which provides higher accuracy and acts as a starting point for the building of the entire model. An alert message is given to the concerned authorities using telegram application.