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U3PT: A New Dataset for Unconstrained 3D Pose Tracking Evaluation

Abstract : 3D pose tracking using monocular cameras is an important topic, which has been receiving a great attention since last decades. It is useful in many domains such as: Video Surveillance, Human-Computer Interface, Biometrics, etc. The problem gets much challenging if occurring, for example, fast motion, out-of-plane rotation, the illumination changes, expression, or occlusions. In the literature, there are some datasets reported for 3D pose tracking evaluation, however, all of them retains simple background, no-expression, slow motion, frontal rotation, or no-occlusion. It is not enough to test advances of in-the-wild tracking. Indeed, collecting accurate ground-truth of 3D pose is difficult because some special devices or sensors are required. In addition, the magnetic sensors usually used for 3D pose ground-truth, is uncomfortable to wear and move because of their wires. In this paper, we propose a new recording system that allows people move more comfortable. We create a new challenging dataset, named U3PT (Unconstrained 3D Pose Tracking). It could be considered as a benchmark to evaluate and compare the robustness and precision of state-of-the-art methods that aims to work in-the-wild. This paper will also present the performances of two well-known state-of-the-art methods compared to our method on face tracking when applied to this database. We have carried out several experiments and have reported advantages and some limitations to be improved in the future.
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Submitted on : Monday, February 22, 2016 - 10:20:19 AM
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Ngoc-Trung Tran, Fakhr-Eddine Ababsa, Maurice Charbit. U3PT: A New Dataset for Unconstrained 3D Pose Tracking Evaluation. 16th International Conference on Computer Analysis of Images and Patterns (CAIP 2015), Sep 2015, Valletta, Malta. pp.642--653, ⟨10.1007/978-3-319-23192-1_54⟩. ⟨hal-01277119⟩



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