Shannon information based adaptive sampling for action recognition

Qing Tian, Tal Arbel, James J. Clark

Abstract: This paper investigates the effects of sampling on action recognition performance. Currently, dense (regular grid) sampling and uniform random sampling are popular strategies that achieve state-of-the-art performance. However, they are data-blind and pay equal attention to locations of different informativeness. In this paper, a Shannon information based adaptive sampling approach is proposed for action recognition. Results of different sampling approaches are compared on three benchmark datasets: the basic KTH and the challenging HMDB51 and UCF101 datasets. The method is shown to improve recognition accuracy as well as computational efficiency over the current state-of-the-art using less than one percent of the total pixels.