A size and position invariant event-based human posture recognition algorithm

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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Bibliographic Details
Main Authors: Chen, Shoushun, Folowosele, Fopefolu, Kim, Dongsoo, Vogelstein, R. Jacob, Etienne-Cummings, Ralph, Culurciello, Eugenio
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2010
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:https://hdl.handle.net/10356/91593
http://hdl.handle.net/10220/6372
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Institution: Nanyang Technological University
Language: English
Description
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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