Robust Human Pose Tracking for Realistic Service Robot Applications

Manolis Vasileiadis, Sotiris Malassiotis, Dimitrios Giakoumis, Christos-Savvas Bouganis, Dimitrios Tzovaras; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1363-1372


Robust human pose estimation and tracking plays an integral role in assistive service robot applications, as it provides information regarding the body pose and motion of the user in a scene. Even though current solutions provide high-accuracy results in controlled environments, they fail to successfully deal with problems encountered under real-life situations such as tracking initialization and failure, body part intersection, large object handling and partial-view body-part tracking. This paper presents a framework tailored for deployment under real-life situations addressing the above limitations. The framework is based on the articulated 3D-SDF data representation model, and has been extended with complementary mechanisms for addressing the above challenges. Extensive evaluation on public datasets demonstrates the framework's state-of-the-art performance, while experimental results on a challenging realistic human motion dataset exhibit its robustness in real life scenarios.

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author = {Vasileiadis, Manolis and Malassiotis, Sotiris and Giakoumis, Dimitrios and Bouganis, Christos-Savvas and Tzovaras, Dimitrios},
title = {Robust Human Pose Tracking for Realistic Service Robot Applications},
booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}