Intelligent Surveillance Framework for Physical Abuse Detection

Abstract

Physical abuse is a societal problem irrespective of whether the society is rich, educated or so. Mostly physically weaker section like children, women and old age people are most vulnerable to it especially in cases of domestic violence or workplace aggression. If the abuser and victim share a relationship then reporting the abuse becomes all the more challenging. Hence an intelligent surveillance system is the need of the hour. In this paper we propose a hybrid deep learning framework for physical abuse detection and prevention through human action recognition. 3D convolution neural network (CNN) based deep learning model is built to analyze this complex human action. Using transfer learning ResNet-18 and GoogleNet model are trained for publicly available UBI fight and UCF crime dataset for detecting physical abuse in the video. The 2D kernels are converted to 3D kernels for both temporal and spatial feature extraction in the video. A layer of bilinear LSTM is added to gather long term temporal information, so that there is improvement in capturing the human action information. This hybrid model used for physical abuse detection is the novel approach and the results show that the performance parameters such as precision, accuracy, specificity and sensitivity score improved as we moved from 2D kernels to 3D, and there is remarkable improvement when bilinear LSTM was added to the deep learning model.