2001 Fiscal Year Final Research Report Summary
Real-time Shape and Posture Estimation of Articulated Objects Based on Multiple kinds of Uncertain Feature Information Obtained from Image Sequences
| Project/Area Number |
11555072
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | Osaka University |
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Principal Investigator |
SHIRAI Yoshiaki Professor, Graduate School of Engineering, Osaka University, 大学院・工学研究科, 教授 (50206273)
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| Co-Investigator(Kenkyū-buntansha) |
SAKIYAMA Takuro Research Associate, Graduate School of Engineering, Osaka University, 大学院・工学研究科, 助手 (70335371)
SHIMADA Nobutaka Research Associate, Graduate School of Engineering, Osaka University, 大学院・工学研究科, 助手 (10294034)
MIURA Jun Associate Professor, Graduate School of Engineering, Osaka University, 大学院・工学研究科, 助教授 (90219585)
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| Project Period (FY) |
1999 – 2001
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| Keywords | hand posture / gesture estimation / articulated object / measuring by image / uncertainty of information / real-time system / human interface |
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| Research Abstract |
The summarized results of this research are as follows:
1. We developed a video-rate hand posture measurement system based on hand region contour image, which consists of high speed PCs with large memories and are connected with each other by G-bit LAN system. The system can estimate almost 160000 possible shapes (including highly self-occluded hand shapes) in real-time using monocular camera setting.
2. We developed an online method to refine the initial shape of the articulated model for posture estimation. The method efficiently describes a huge set of the possible posture parameters employing a multi-dimensional ellisoid description. The method can refine the given initial model shape after the posture estimation in each timestep from a monocular image sequences, while it is still computationally heavy process and cannot be processed in real-time under the current computing environment.
3. We proposed a recognition method of Japanese Sign Language words using multiple camera images, color feature, image motion flows and observed number of the finger tips. Although each of these image features has uncertainty, we developed the system which integrates whole of the information using Hidden Markov Model learning and implements it into a gestural interface.
4. We proposed a novel time-space gestural model based on Switching Linear Dynamics architecture. Based on this model, we developed an on-line method to segment a shape-changing human hand region from a complicated background and identify it from several gesture models.
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