Situation recognition using image moments and recurrent neural networks

Surveillance is an important need for a secured and supervised environment. Manual supervision for the purpose of surveillance proves to be expensive and prone to slipups. Many researchers have worked to provide an automated solution to this problem. In this article, we present a solution to this problem using image moments and recurrent neural networks. For this purpose, frames are first extracted from a live video and the foreground of the frame is sieved out while the background is discarded. Feature vectors are obtained for each frame after computing raw, central, scale-invariant and rotation-invariant moments of the images. These vectors are used to train and ultimately simulate a recurrent neural network. The results generated from this model exhibit an accuracy of 96.4 % in identification of events within consecutive frames.