Abnormal Activity Recognition using Deep Learning in Streaming Video for Indoor Application

Human activity recognition has emerged as a challenging research domain for video analysis. The major issue for abnormal activity recognition in a streaming video is the presence of the large spatio-temporal data along with the constraints of communication networks affecting the quality of received data for analysis. In this paper, we propose a deep learning-based system to identify abnormal human activities using a combination of Skeleton Activity Forecasting (SAF) and a Bi-LSTM network. The generated skeleton joint points of a human subject are used for the pose estimation. The skeleton tracking and regions of interest points are estimated on a streaming video from an IP networked camera. The extracted interest points and their corresponding features are optimized and used to classify them as normal, abnormal or suspicious actions. The proposed system complies with Recommendation ITU-T H.627 “Signalling and protocols for a video surveillance system” and has been experimented and evaluated over benchmarked data sets for the recognition of human actions. The system performance attains a precision of 85.6% and an accuracy of 97.2% in recognizing different actions.