Grasp type understanding - classification, localization and clustering
Li, Yinlin; Zhang, Yuren; Qiao, Hong; Chen, Ken; Xi, Xuanyang; Li, YL
2016
Conference Name12th World Congress on Intelligent Control and Automation (WCICA)
Source PublicationPROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA)
Conference DateJUN 12-15, 2016
Conference PlaceGuilin, PEOPLES R CHINA
AbstractPrehensile analysis is a research field attracting multi-disciplinary interests, including computer science, mechanology and neuroscience. For robot, grasp type recognition provides critical information for human-robot interaction and robot self-learning. One of the research direction is to discover the common modes of human hand use with first-person point-of-view wearable cameras. In contrast to previous methods based on handcraft features and multi-stage pipeline, we use a convolutional neural network to learn discriminative features of grasp types automatically, which can also achieve grasptype localization and classification simultaneously in a single-stage pipeline. Furthermore, a clusteringmethod is also proposed to find the hierarchical relationships between different grasp types. Experiments are conducted on UT Grasp dataset and Yale human grasping dataset. The proposed method shows better accuracy and higher efficiency than traditional methods.
KeywordObjects Hands
Subject AreaAutomation & Control Systems ; Engineering, Electrical & Electronic
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12825
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorLi, YL
Recommended Citation
GB/T 7714
Li, Yinlin,Zhang, Yuren,Qiao, Hong,et al. Grasp type understanding - classification, localization and clustering[C],2016.
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