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Confidence-driven architecture for real-time vision processing and its application to efficient vision-based human motion sensing
In this paper, we discuss a real-time vision architecture which provides a mechanism of controlling trade-off between the accuracy and the latency of vision systems. In vision systems, to acquire accurate information from input-images, the huge amount of computation power is usually required. On the...
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Main Authors: | , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | In this paper, we discuss a real-time vision architecture which provides a mechanism of controlling trade-off between the accuracy and the latency of vision systems. In vision systems, to acquire accurate information from input-images, the huge amount of computation power is usually required. On the other hand, to realize real-time processing, we must reduce the latency. Therefore, under given hardware resources, we must make difficult trade-off between the accuracy and the latency so that the quality of the system's output keeps appropriateness. To solve the problem, we propose confidence-driven scheme, which enables us to control the trade-off dynamically and easily without rebuilding vision systems. In the confidence-driven architecture, the trade-off can be controlled by specifying a generalized parameter called confidence, which relatively indicates how accurate the analysis should be. Here, we present the concept of confidence-driven architecture, and then, we show a shared memory which uses confidence-driven scheme. Using confidence-driven memory, we can use imprecise computation model to reduce the latency without a large decrease of accuracy. |
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ISSN: | 1051-4651 2831-7475 |
DOI: | 10.1109/ICPR.2004.1334294 |