A Comprehensive Study and Detection of Anomalies for Autonomous Video Surveillance Using Neuromorphic Computing and Self Learning Algorithm

Abstract

Video Analytics is widely applied in the field of surveillance. Recently, with the advent in technology deep learning network has been incorporated in the video action detection. Traditional CNN is employed to extract 2D spatial features of image but for video it is required to exploit CNN for temporal information. In this work we propose to do instance segmentation in video bytes and predicting the actions with the help of deep learning. And, we aim to present an implementation of an algorithm that can depict anomalies in real time video feed.