A VGA 30-fps Realtime Optical-Flow Processor Core for Moving Picture Recognition
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- MURACHI Yuichiro
- Kobe University The Institute of Electronics, Information and Communication Engineers
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- FUKUYAMA Yuki
- Kobe University The Institute of Electronics, Information and Communication Engineers
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- YAMAMOTO Ryo
- Kobe University The Institute of Electronics, Information and Communication Engineers
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- MIYAKOSHI Junichi
- Kobe University The Institute of Electronics, Information and Communication Engineers
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- KAWAGUCHI Hiroshi
- Kobe University
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- ISHIHARA Hajime
- Kanazawa University
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- MIYAMA Masayuki
- Kanazawa University The Institute of Electronics, Information and Communication Engineers
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- MATSUDA Yoshio
- Kanazawa University
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- YOSHIMOTO Masahiko
- Kobe University The Institute of Electronics, Information and Communication Engineers
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Abstract
This paper describes an optical-flow processor core for real-time video recognition. The processor is based on the Pyramidal Lucas and Kanade (PLK) algorithm. It features a smaller chip area, higher pixel rate, and higher accuracy than conventional optical-flow processors. Introduction of search range limitation and the Carman filter to the original PLK algorithm improve the optical-flow accuracy, and reduce the processor hardware cost. Furthermore, window interleaving and window overlap methods reduces the necessary clock frequency of the processor by 70%, allowing low-power characteristics. We first verified the PLK algorithm and architecture with a proto-typed FPGA implementation. Then, we designed a VLSI processor that can handle a VGA 30-fps image sequence at a clock frequency of 332MHz. The core size and power consumption are estimated at 3.50×3.00mm2 and 600mW, respectively, in a 90-nm process technology.
Journal
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- IEICE Transactions on Electronics
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IEICE Transactions on Electronics E91-C (4), 457-464, 2008
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390282679352932608
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- NII Article ID
- 10026817372
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- NII Book ID
- AA10826283
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- ISSN
- 17451353
- 09168524
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed