A Fast Statistical Approach for Human Activity Recognition

作     者：Samy Sadek Ayoub Al-Hamadi Bernd Michaelis Usama Sayed 

作者机构：不详 

出 版 物：《International Journal of Intelligence Science》 (智能科学国际期刊（英文）)

年 卷 期：2012年第2卷第1期

页      面：9-15页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Activity Recognition Motion Analysis Statistical Moments Video Interpretation 

摘      要：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.