We present an algorithm for analyzing the human body 3-D motion of golf swing from single-camera video sequences. As the first step, the human body used for the analysis is automatically extracted using a video object segmentation technique. Once human body is extracted, this twodimensional information is utilized to obtain a threedimensional body model consisting of head, upper arms, lower arms, body trunk, upper legs, lower legs and feet using an iterative 3-D fitting algorithm and Dynamic Bayesian Networks. The ultimate objective of the system is to obtain the 3-D motion information for golf swinging and comparing this information for different players regardless of the great variability caused by different camera viewing perspectives. 3-D body motion during the golf swing is computed for all segments of the body. This system will allow the spatial-temporal relationship of each body segment, as they make their transition, be thoroughly studied, and enable the parameters for different players to be compared, as well.

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We present a framework for analyzing the human body 3-D motion of golf swing from single-camera video sequences. The system is different from the methods in the literature as it evaluates the 3D model for each major body part separately to derive a more accurate 3D representation. The human body parts used for the analysis are automatically extracted using a video object segmentation technique. This two-dimensional information estimated from the segmented body parts is utilized to obtain threedimensional body part models, consisting of head, upper arms, lower arms, body trunk, upper legs, lower legs and feet. The objective of the system is to obtain the 3-D motion information for performance evaluation in golf swinging and comparing this information for different players.

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We present a novel fully automatic moving video object extraction algorithm based on graph partitioning and object tracking. A sophisticated graph partitioning algorithm is first used to (intra-frame) segment the moving video objects (Vas) of a specific (key) frame without any prior knowledge of segmentation results of previous frame(s). Once the vas for this key frame are extracted, the (inter-frame) segmentation of vas of subsequent frames is accomplished by tracking segmented objects of consecutive frames by means of motion projection and modified histogram back-projection. To avoid significant error propagation caused by the (inter-frame) object tracking, a new key frame has to be identified periodically, based on a criterion in terms of camera motion parameters.Simulation results illustrate that the proposed algorithm gives comparable results to semi-automatic methods introduced in the literatures.