Quantifying and Recognizing Human Movement Patterns From Monocular Video Images—Part I: A New Framework for Modeling Human Motion

Abstract

Research into tracking and recognizing human movement has so far been mostly limited to gait or frontal posing. Part I of this paper presents a continuous human movement recognition (CHMR) framework which forms a basis for the general biometric analysis of continuous human motion as demonstrated through tracking and recognition of hundreds of skills from gait to twisting saltos. Part II of this paper presents CHMR applications to the biometric authentication of gait, anthropometric data, human activities, and movement disorders. In Part I of this paper, a novel three-dimensional color clone-body-model is dynamically sized and texture mapped to each person for more robust tracking of both edges and textured regions. Tracking is further stabilized by estimating the joint angles for the next frame using a forward smoothing particle filter with the search space optimized by utilizing feedback from the CHMR system. A new paradigm defines an alphabet of dynemes, units of full-body movement skills, to enable recognition of diverse skills. Using multiple hidden Markov models, the CHMR system attempts to infer the human movement skill that could have produced the observed sequence of dynemes. The novel clone-body-model and dyneme paradigm presented in this paper enable the CHMR system to track and recognize hundreds of full-body movement skills, thus laying the basis for effective biometric authentication associated with full-body motion and body proportions.