Fast and reliable human action recognition in video sequences by sequential analysis

Human action recognition from video sequences is a challenging topic in computer vision research. In recent years, many studies have explored the use of deep learning representations to consistently improve the analysis accuracy. Meanwhile, designing a fast and reliable framework is becoming increasingly important given the exponential growth of video data collected for many purposes (e.g. public security, entertainment, and early medical diagnosis etc.). In order to design a more efficient automatic human action annotation method, the sequential probability ratio test, one of the classical statistical sampling scheme, is adapted to solve a multi-classes hypothesis test problem in our work. With the proposed algorithm, the computational cost is reduced significantly without sacrificing the performance of the underlying system. The experimental results based on the UCF101 data set demonstrated the efficiency of the framework compared to the fixed sampling scheme.