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http://bura.brunel.ac.uk/handle/2438/5960
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DC Field | Value | Language |
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dc.contributor.author | Meng, H | - |
dc.contributor.author | Freeman, M | - |
dc.contributor.author | Pears, N | - |
dc.contributor.author | Bailey, C | - |
dc.date.accessioned | 2011-11-15T16:28:44Z | - |
dc.date.available | 2011-11-15T16:28:44Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | Journal of Real-Time Image Processing, 3(3): 163 - 176, 2008 | en_US |
dc.identifier.issn | 1861-8200 | - |
dc.identifier.uri | http://www.springerlink.com/content/v357q13703pr47r0/ | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/5960 | - |
dc.description | Copyright @ 2008 Springer-Verlag. | en_US |
dc.description.abstract | In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments. | en_US |
dc.description.sponsorship | DTI and Broadcom Ltd. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.subject | Embedded devices | en_US |
dc.subject | FPGA | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Machine learning | en_US |
dc.title | Real-time human action recognition on an embedded, reconfigurable video processing architecture | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s11554-008-0073-1 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel (Active) | - |
pubs.organisational-data | /Brunel/Brunel (Active)/School of Engineering & Design | - |
Appears in Collections: | Electronic and Electrical Engineering Publications Dept of Electronic and Electrical Engineering Research Papers |
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Fulltext.pdf | 1.37 MB | Adobe PDF | View/Open |
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