Extracting Refined Low-Rank Features of Robust PCA for Human Action Recognition

Motion representation is a challenging task in human action recognition. To represent motion, most traditional methods usually require certain intermediate processing steps such as actor segmentation, body tracking, and interest point detection, which make these methods sensitive to errors caused by these processing steps. In this paper, motivated by the successful recovery of low-rank matrix using robust principal component analysis (RPCA), we present a novel motion representation method for action recognition by extracting refined low-rank features of RPCA. Compared with the traditional methods, our method does not require the intermediate processing steps mentioned above. Unfortunately, with traditional λ, RPCA is incapable of extracting the discriminative information of motion in action videos, thus we first conduct extensive experiments to determine a feasible parameter λ suitable for action recognition. Then, we perform RPCA with this λ to obtain the low-rank images including the discriminative information of motion. To represent characteristic of the obtained low-rank images, we define two descriptors [i.e., edge distribution histogram (EDH) and accumulated edge distribution histogram (AEDH)] to refine the low-rank images. Finally, a support vector machine is trained to classify human actions represented by EDH or AEDH features. The efficacy of the proposed method is verified on three public datasets, and experimental results have shown the promising results of our method for human action recognition.