期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2012
卷号：4
页码：334-340
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：An essential part of any activity recognition system claiming be truly real-time is the ability to perform feature ex- traction in real-time. We present, in this paper, a quite sim- ple and computationally tractable approach for real-time hu- man activity recognition that is based on simple statistical fea- tures. These features are simple and relatively small, accord- ingly they are easy and fast to be calculated, and further form a relatively low-dimensional feature space in which classifica- tion 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 and embedded systems.
关键词：Activity recognition; motion analysis; statistical mo- ; ments; video interpretation
