This note introduces a model-free and marker-less approach for human body tracking based on a dynamic color model and geometric information of a human body from a monocular video sequence. A multivariate Gaussian distribution is learned online from sequential frames to represent the non-stationary color distribution. Images are first filtered according to the current color model allowing a human body to be segmented from a background. Next, the segmented image is partitioned based on geometric prior knowledge of human structure and each body part of interest is separately tracked. Finally, eigenaxis based joint angle estimation is carried out to evaluate jumping and landing posture. The resulting data facilitates motion analysis of a specific set of clinically interesting quantities that allow post-operative evaluation and correlation of injury statistics with a subject’s mechanics. The proposed approach is tested on the different views of human jumping and landing sequences in a noisy and cluttered environment with video from a mobilephone camera. Experimental results demonstrate the practical applicability and robustness of the proposed algorithm in tracking human motion captured with a monocular camera system.