Human motion tracking from monocular image sequences is a challenging work in computer vision. It also has many penitential applications

such as human computer interface

computer animation

and intelligent video surveillance. Methodologies of example-based human motion tracking from monocular camera are explored in this study to meet timeliness

accuracy

and reliability requirements of human motion tracking for real applications. We focus on two main aspects: visual feature extraction and human motion modeling. Based on an example database that is constructed with motion capture data

an example-based approach for human pose estimation from monocular image sequences is proposed. First

we use a motion detection method to extract human region from images. Then

an edge-tracking method is used to detect human silhouette from human region. Second

shape context is used to describe the human silhouette detected from video frames

and candidate poses are obtained from the example database by silhouette matching. Third

we build probability and statistical model of human motion and conducted pose estimation from these candidates. Experimental results on walking

running

and jumping videos demonstrate that shape context-based silhouette representation and matching method can effectively extract human visual feature from image. Compared with other methods

the proposed method can tackle orientation ambiguity problem effectively. Moreover

it is invariant to viewpoints. In this paper

we proposed an example-based method for human motion analysis. Shape context is used for visual feature extraction and matching. Probability and statistical model of motion are used for pose estimation. Experimental results on different types of motion demonstrate that the proposed method can analyze 3D pose from videos effectively

thereby increasing the efficiency and accuracy of human motion analysis. Moreover

the proposed method can solve the orientation ambiguity problem

and is invariant to viewpoints.