Key-Pose Prediction in Cyclic Human Motion

In this paper we study the problem of estimating innercyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of poselets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.Comment: Accepted at WACV 2015, 8 pages, 3 figure

A kinematic model for Bayesian tracking of cyclic human motion

We introduce a two-dimensional kinematic model for cyclic motions of humans, which is suitable for the use as temporal prior in any Bayesian tracking framework. This human motion model is solely based on simple kinematic properties: the joint accelerations. Distributions of joint accelerations subject to the cycle progress are learned from training data. We present results obtained by applying the introduced model to the cyclic motion of backstroke swimming in a Kalman filter framework that represents the posterior distribution by a Gaussian. We experimentally evaluate the sensitivity of the motion model with respect to the frequency and noise level of assumed appearance-based pose measurements by simulating various fidelities of the pose measurements using ground truth data