Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor

Abstract

This paper presents a template-based algorithm to track and recognize athlete’s actions in an integrated system using only visual information. Conventional template-based action recognition systems usually consider action recognition and tracking as two independent problems, and solve them separately. In contrast, our algorithm emphasizes that tracking and action recognition can be tightly coupled into a single framework, where tracking assists action recognition and vise versa. Moreover, this paper proposes to represent the athletes by the PCA-HOG descriptor, which can be computed by first transforming the athletes to the grids of Histograms of Oriented Gradient (HOG) descriptor and then project it to a linear subspace by Principal Component Analysis (PCA). The exploitation of the PCA-HOG descriptor not only helps the tracker to be robust under illumination, pose, and view-point changes, but also implicitly centers the figure in the tracking region, which makes action recognition possible. Empirical results in hockey and soccer sequences show the effectiveness of this algorithm.