• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.255-262
• /
• 2012

Human motion tracking algorithm is receiving attention from many research areas, such as human computer interaction, video conference, surveillance analysis, and game or entertainment applications. Over the last decade, various tracking technologies for each application have been demonstrated and refined among them such of real time computer vision and image processing, advanced man-machine interface, and so on. In this paper, we introduce cost-effective and real-time human motion tracking algorithms based on depth image 3D point matching with a given superellipsoid body representation. The body representative model is made by using parametric volume modeling method based on superellipsoid and consists of 18 articulated joints. For more accurate estimation, we exploit initial inverse kinematic solution with classified body parts' information, and then, the initial pose is modified to more accurate pose by using 3D point matching algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.314-314
• /
• 2000

This study deals with the analysis of the effectiveness of a safer belt in frontal crash. ATB, Articulated Total Body, program is used as a dynamics solver of the occupant model. ATB is a public code, however, the program is somewhat cumbersome to use due to lack of sufficient user interface. A preprocessor and a postprocessor are, therefore, developed for a user friendly graphic interface in Windows environment. Dialog boxes are used for an interface with GEBOD, Generator of Body Data, for human anthropometry and with ADAMS for vehicle dynamics. It is found through three test simulations that simulated results are in good agreement with those obtained by ATB. The effect of the initial slack of safety belt is investigated for frontal crash using the developed program.

• Journal of the Korea Computer Graphics Society
• /
• v.21 no.4
• /
• pp.19-26
• /
• 2015

This paper presents a method for optimizing a carrying pose of human body for a given object. The inputs are articulated human body model and and arbitrary-shaped object. We assume that the object is big and heavy, so that both arms should be used to carry it. Unlike small and light objects, big and heaby objects can be hold by only a small range of body poses while keeping a physical statbility. We first introduce an algorithm that evaluates a physical stability of a given human body pose and object state (position and orientation). Then, we define a configuration space and search the space for the most stable carrying pose by using the evaluation algorithm. Finally, to demonstrate the usability of our method, we present the results which each is experimented with different shaped objects and additional user conditions.

• The KIPS Transactions:PartB
• /
• v.18B no.1
• /
• pp.11-20
• /
• 2011

Pictorial Structures is known as an effective method that recognizes and tracks human poses. In this paper, the upper body pose is also tracked by PS and a particle filter(PF). PF is one of dynamic programming methods. But Markov chain-based dynamic motion model which is used in dynamic programming methods such as PF, couldn't predict effectively the highly articulated upper body motions. Therefore PF often fails to track upper body pose. In this paper we propose the key pose-based proposal distribution for proper particle prediction based on the similarities between key poses and an upper body silhouette. In the experimental results we confirmed our 70.51% improved performance comparing with a conventional method.

• Journal of the Korea Computer Graphics Society
• /
• v.20 no.2
• /
• pp.1-11
• /
• 2014

The quality of 2D-to-3D conversion depends on the accuracy of the assigned depth to scene objects. Manual depth painting for given objects is labor intensive as each frame is painted. Specifically, a human is one of the most challenging objects for a high-quality conversion, as a human body is an articulated figure and has many degrees of freedom (DOF). In addition, various styles of clothes, accessories, and hair create a very complex silhouette around the 2D human object. We propose an efficient method to estimate visually pleasing depths of a human at every frame in a monocular video. First, a 3D template model is matched to a person in a monocular video with a small number of specified user correspondences. Our pose estimation with sequential joint angular constraints reproduces a various range of human motions (i.e., spine bending) by allowing the utilization of a fully skinned 3D model with a large number of joints and DOFs. The initial depth of the 2D object in the video is assigned from the matched results, and then propagated toward areas where the depth is missing to produce a complete depth map. For the effective handling of the complex silhouettes and appearances, we introduce a partial depth propagation method based on color segmentation to ensure the detail of the results. We compared the result and depth maps painted by experienced artists. The comparison shows that our method produces viable depth maps of humans in monocular videos efficiently.