A Fast Statistical Approach for Human Activity Recognition
Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed
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DOI: 10.4236/ijis.2012.21002   PDF    HTML     5,812 Downloads   14,010 Views   Citations

Abstract

An essential part of any activity recognition system claiming be truly real-time is the ability to perform feature extraction in real-time. We present, in this paper, a quite simple and computationally tractable approach for real-time human activity recognition that is based on simple statistical features. These features are simple and relatively small, accordingly they are easy and fast to be calculated, and further form a relatively low-dimensional feature space in which classification can be carried out robustly. On the Weizmann publicly benchmark dataset, promising results (i.e. 97.8%) have been achieved, showing the effectiveness of the proposed approach compared to the-state-of-the-art. Furthermore, the approach is quite fast and thus can provide timing guarantees to real-time applications.

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S. Sadek, A. Al-Hamadi, B. Michaelis and U. Sayed, "A Fast Statistical Approach for Human Activity Recognition," International Journal of Intelligence Science, Vol. 2 No. 1, 2012, pp. 9-15. doi: 10.4236/ijis.2012.21002.

Conflicts of Interest

The authors declare no conflicts of interest.

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