ATOMIC HUMAN ACTION SEGMENTATION AND RECOGNITION USING A SPATIO-TEMPORAL PROBABILISTIC FRAMEWORK

Abstract

In this paper, a framework of automatic human action segmentation and recognition in continuous action sequences is proposed. A star figure enclosed by a bounding convex polygon is used to effectively represent the extremities of the silhouette of a human body. The human action, thus, is recorded as a sequence of the star-figure's parameters, which is used for action modeling. To model human actions in a compact manner while characterizing their spatio-temporal distributions, the star-figure's parameters are represented by Gaussian mixture models (GMM). In addition, to address the intrinsic nature of temporal variations in a continuous action sequence, we transform the time sequence of star-like figure parameters into frequency domain by discrete cosine transform (DCT) and use only the first few coefficients to represent different temporal patterns with significant discriminating power. The performance shows that the proposed framework can recognize continuous human actions in an efficient way.