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FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture

By Hongying Meng, Micheal Freeman, Nick Pears and Chris Bailey


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 reconfigured 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

Topics: G411 Computer Architectures, G760 Machine Learning, G400 Computer Science, G740 Computer Vision
Publisher: Springer
Year: 2008
OAI identifier: oai:eprints.lincoln.ac.uk:1974

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