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Real-time human action recognition on an embedded, reconfigurable video processing architecture

Meng, H; Freeman, M; Pears, N; Bailey, C; (2008) Real-time human action recognition on an embedded, reconfigurable video processing architecture. Journal of Real-Time Image Processing , 3 (3) 163 - 176. 10.1007/s11554-008-0073-1. Green open access

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Abstract

In recent years, automatic human action recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time, embedded vision solution for human action recognition, implemented on an FPGA-based ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human action recognition system with simple motion features and a linear support vector machine classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template class of approaches, which include “Motion History Image” based techniques. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human action recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is operating reliably at 12 frames/s, 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.

Type: Article
Title: Real-time human action recognition on an embedded, reconfigurable video processing architecture
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11554-008-0073-1
Publisher version: http://dx.doi.org/10.1007/s11554-008-0073-1
Language: English
Additional information: The original publication is available at www.springerlink.com
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
URI: https://discovery-pp.ucl.ac.uk/id/eprint/239921
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