ARMA-HMM: A new approach for early recognition of human activity

Early Recognition of human activities is a highly desirable functionality for many visual intelligent systems. However, in computer vision, very few work have been devoted to this challenging and interesting task. In this paper, we address human activity early recognition as a pattern recognition problem of time series data. A new model called ARMA-HMM is introduced to integrate both the predictive power of sequential model HMM and time series model ARMA. We also present a novel feature called Histogram of Oriented Velocity (HOV) to encode activity video as a sequential observation of motion signals. Experiments on a daily activity dataset and a realistic YouTube sports dataset show promising results of the proposed method.