A size and position invariant event-based human posture recognition algorithm
In this paper we report a size and position invariant human posture recognition algorithm. The algorithm employs a simplified line segment Hausdorff distance classification and uses projection histograms to achieve size and position invariance....
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Main Authors: | , , , , , |
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Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2010
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/91593 http://hdl.handle.net/10220/6372 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | In this paper we report a size and position invariant
human posture recognition algorithm. The algorithm employs a
simplified line segment Hausdorff distance classification and uses
projection histograms to achieve size and position invariance.
Compared to other existing method utilizing line segment Hausdorff
distance, the proposed algorithm reduces the computation
complexity by 36000 times, for our test images. Combining bioinspired
event-based image acquisition and hardware friendly
feature extraction and classification algorithm will lead to a
promising technology for use in wireless sensor network. |
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