Anomalous Activity Detection from Ego View Camera of Surveillance Robots

Abstract

Can a surveillance robot autonomously detect anomalous activity from its ego view camera perception? This is a challenging task as it requires identifying what is normal and what is an abnormal pattern - given the variations of possible anomalies and abnormalities. This paper presents an architecture and method based on a spatio-temporal convolution neural network to detect and classify anomalies. This work is inspired by the ‘Konio-Magno-Parvocellular’ cells of the human brain, which is claimed to aid humans in organizing changes in perceived scenes. The model is trained and tested on a benchmark video dataset [1] of human activity. We have obtained 91% testing accuracy on this dataset. Experiments in simulation as well as deployment on a real robot shows that the proposed methodology can identify anomalous activities effectively. We have also listed down the observations from practical deployment of the model.