Monocular 3D Pose Estimation and Tracking by Detection

by Mykhaylo Andriluka, Stefan Roth, Bernt Schiele
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2010) ()

Abstract

Automatic recovery of 3D human pose from monocular image sequences is a challenging and important research topic with numerous applications. Although current meth- ods are able to recover 3D pose for a single person in con- trolled environments, they are severely challenged by real- world scenarios, such as crowded street scenes. To address this problem, we propose a three-stage process building on a number of recent advances. The first stage obtains an ini- tial estimate of the 2D articulation and viewpoint of the per- son from single frames. The second stage allows early data association across frames based on tracking-by-detection. These two stages successfully accumulate the available 2D image evidence into robust estimates of 2D limb positions over short image sequences (= tracklets). The third and final stage uses those tracklet-based estimates as robust im- age observations to reliably recover 3D pose. We demon- strate state-of-the-art performance on the HumanEva II benchmark, and also show the applicability of our approach to articulated 3D tracking in realistic street conditions.

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