Browsing by Author "Qin, Xueying"

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    3D Object Tracking for Rough Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Song, Xiuqiang; Xie, Weijian; Li, Jiachen; Wang, Nan; Zhong, Fan; Zhang, Guofeng; Qin, Xueying; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Visual monocular 6D pose tracking methods for textureless or weakly-textured objects heavily rely on contour constraints established by the precise 3D model. However, precise models are not always available in reality, and rough models can potentially degrade tracking performance and impede the widespread usage of 3D object tracking. To address this new problem, we propose a novel tracking method that handles rough models. We reshape the rough contour through the probability map, which can avoid explicitly processing the 3D rough model itself. We further emphasize the inner region information of the object, where the points are sampled to provide color constrains. To sufficiently satisfy the assumption of small displacement between frames, the 2D translation of the object is pre-searched for a better initial pose. Finally, we combine constraints from both the contour and inner region to optimize the object pose. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on both roughly and precisely modeled objects. Particularly for the highly rough model, the accuracy is significantly improved (40.4% v.s. 16.9%).
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    Accompany Children's Learning for You: An Intelligent Companion Learning System
    (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Qian, Jiankai; Jiang, Xinbo; Ma, Jiayao; Li, Jiachen; Gao, Zhenzhen; Qin, Xueying; Hauser, Helwig and Alliez, Pierre
    Nowadays, parents attach importance to their children's primary education but often lack time and correct pedagogical principles to accompany their children's learning. Besides, existing learning systems cannot perceive children's emotional changes. They may also cause children's self‐control and cognitive problems due to smart devices such as mobile phones and tablets. To tackle these issues, we propose an intelligent companion learning system to accompany children in learning English words, namely the . The IARE realizes the perception and feedback of children's engagement through the intelligent agent (IA) module, and presents the humanized interaction based on projective Augmented Reality (AR). Specifically, IA perceives the children's learning engagement change and spelling status in real‐time through our online lightweight temporal multiple instance attention module and character recognition module, based on which analyses the performance of the individual learning process and gives appropriate feedback and guidance. We allow children to interact with physical letters, thus avoiding the excessive interference of electronic devices. To test the efficacy of our system, we conduct a pilot study with 14 English learning children. The results show that our system can significantly improve children's intrinsic motivation and self‐efficacy.
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    An Occlusion-aware Edge-Based Method for Monocular 3D Object Tracking using Edge Confidence
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Huang, Hong; Zhong, Fan; Sun, Yuqing; Qin, Xueying; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    We propose an edge-based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge-based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge-based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge-based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi-object tracking which can handle mutual occlusions. We compare our method with the recent state-of-art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions.