Human Motion Tracking by Combining View-based and Model-based Methods for Monocular Video Sequences 


Vol. 10,  No. 6, pp. 657-664, Oct.  2003
10.3745/KIPSTB.2003.10.6.657


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  Abstract

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classify individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

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[IEEE Style]

P. J. Heon and P. S. Ho, "Human Motion Tracking by Combining View-based and Model-based Methods for Monocular Video Sequences," The KIPS Transactions:PartB , vol. 10, no. 6, pp. 657-664, 2003. DOI: 10.3745/KIPSTB.2003.10.6.657.

[ACM Style]

Park Ji Heon and Park Sang Ho. 2003. Human Motion Tracking by Combining View-based and Model-based Methods for Monocular Video Sequences. The KIPS Transactions:PartB , 10, 6, (2003), 657-664. DOI: 10.3745/KIPSTB.2003.10.6.657.

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