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

Hongying Meng, Nick Pears, Chris Bailey, Mike Freeman

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)163-176
Number of pages14
Issue number3
Publication statusPublished - Sept 2008


  • Human motion recognition
  • Reconfigurable architectures
  • Embedded computer vision
  • FPGA
  • Machine learning

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