Abstract
One of the most important issues in human motion analysis is the tracking and 3D reconstruction of human motion, which utilizes the anatomic points' positions. These points can uniquely define the position and orientation of all anatomical segments. In this work, a new method is proposed for tracking and 3D reconstruction of human motion from the image sequence of a monocular static camera. In this method, 2D tracking is used for 3D reconstruction, which a database of selected frames is used for the correction of tracking process. The method utilizes a new image descriptor based on discrete cosine transform (DCT), which is employed in different stages of the algorithm. The advantage of using this descriptor is the capabilities of selecting proper frequency regions in various tasks, which results in an efficient tracking and pose matching algorithms. The tracking and matching algorithms are based on reference descriptor matrixes (RDMs), which are updated after each stage based on the frequency regions in DCT blocks. Finally, 3D reconstruction is performed using Taylor’s method. Experimental results show the promise of the algorithm.
Highlights
One of the challenging issues in machine vision and computer graphic applications is the modeling and animation of human characters
Body modeling using video sequences is a difficult task that has been investigated a lot in the last decade
The advantage of using reference descriptor matrixes (RDMs) is the capabilities of selecting proper frequency regions in various tasks, which results in an efficient tracking and posematching algorithms
Summary
One of the challenging issues in machine vision and computer graphic applications is the modeling and animation of human characters. Model-free approaches mostly use a database of exemplars [9] or a learning machine [10, 11] for motion reconstruction. Different algorithms for motion reconstruction using monocular videos are roughly divided into three categories, including, (i) discriminative methods [9, 13, 14], (ii) estimating and tracking methods [6,7,8], and (iii) method based on learning [4, 11]. Various algorithms for human motion reconstruction may utilize different image descriptors for tracking, matching, or model extraction. We introduce a new method for 3D reconstruction of human motion in uncalibrated monocular video streams, which is based on our previous work [25]. We review the human model utilized in the proposed algorithm
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