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Proceedings Paper

Particle swarm optimization on low dimensional pose manifolds for monocular human pose estimation
Author(s): Jürgen Brauer; Wolfgang Hübner; Michael Arens
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Paper Abstract

Automatic assessment of situations with modern security and surveillance systems requires sophisticated discrimination capabilities. Therefore, action recognition, e.g. in terms of person-person or person-object interactions, is an essential core component of any surveillance system. A subclass of recent action recognition approaches are based on space time volumes, which are generated from trajectories of multiple anatomical landmarks like hands or shoulders. A general prerequisite of these methods is the robust estimation of the body pose, i.e. a simplified body model consisting of several anatomical landmarks. In this paper we address the problem of estimating 3D poses from monocular person image sequences. The first stage of our algorithm is the localization of body parts in the 2D image. For this, a part based object detection method is used, which in previous work has been shown to provide a sufficient basis for person detection and landmark estimation in a single step. The output of this processing step is a probability distribution for each landmark and image indicating possible locations of this landmark in image coordinates. The second stage of our algorithm searches for 3D pose estimates that best t to the 15 landmark probability distributions. For resolving ambiguities introduced by uncertainty in the locations of the landmarks, we perform an optimization within a Particle Swarm Optimization (PSO) framework, where each pose hypothesis is represented by a particle. Since the search in the high-dimensional 3D pose search space needs further guidance to deal with the inherently restricted 2D input information, we propose a new compact representation of motion sequences provided by motion capture databases. Poses of a motion sequence are embedded in a low-dimensional manifold. We represent each motion sequence by a compact representation referred to as pose splines using a small number of supporting point poses. The PSO algorithm can be extended to perform the optimization process directly on pose splines. Results of the proposed method are shown on the UMPM benchmark.

Paper Details

Date Published: 16 October 2013
PDF: 15 pages
Proc. SPIE 8901, Optics and Photonics for Counterterrorism, Crime Fighting and Defence IX; and Optical Materials and Biomaterials in Security and Defence Systems Technology X, 89010D (16 October 2013); doi: 10.1117/12.2028702
Show Author Affiliations
Jürgen Brauer, Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation (Germany)
Wolfgang Hübner, Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation (Germany)
Michael Arens, Fraunhofer Institute for Optronics, System Technologies, and Image Exploitation (Germany)


Published in SPIE Proceedings Vol. 8901:
Optics and Photonics for Counterterrorism, Crime Fighting and Defence IX; and Optical Materials and Biomaterials in Security and Defence Systems Technology X
Roberto Zamboni; Douglas Burgess; Gari Owen; Francois Kajzar; Attila A. Szep, Editor(s)

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