Human motion classification using 2D stick-model matching regression coefficients

Motion estimation methods have been proposed via different approaches, such as silhouette based, model based and image based estimations. However, these methods are highly dependent on the quality of motion data for optimal classification accuracy. Further, because of the complexity of existing algorithms for motion estimation, there are difficulties in interpretation. Hence, the contribution of this work is to model simple human motions for the purpose of recognizing different activity behavior patterns for classification analysis. The model is made up of three body component integrations – Backbone (BB), Upper Body (UB) and Lower Body (LB) – to form a simple 2D human stick figure. Two case studies involving a publicly available video of walking, running and jumping motions as well as experimental captures of Yoga motions are studied. Video motions are simplified into time-step image snapshots, which are later translated into a numeric 2D coordinate system. Initially, the human pelvis is considered the origin of the stick figure. The stick model was drawn by integrating the BB, UB and LB components based on the 2D body joint coordinates. The motion estimation model applies the concept of polynomial fitting to the coordinates data. Computations on the polynomial fitting coefficient deviations at sequential time steps were performed to evaluate the estimation tolerance. A summation of the precedent time-step coordinates with the average deviation metric is used iteratively to estimate the joint coordinates of the stick figure in the subsequent time step to develop the entire motion model. Finally, the developed motion estimation mathematical model was compared to the actual motion phases for classification efficiencies using the Bayes, Lazy, Function, Meta, Misc, Rules and Trees classifiers. Our findings revealed the feasibility of using 2D stick-model matching estimation for human motion classification analysis.