A Fast Statistical Approach for Human Activity Recognition

• 作者: Ayoub Al-Hamadi Bernd Michaelis Samy Sadek Usama Sayed
• 来源期刊: 智能科学国际期刊(英文)
• 学科: 工学
• 关键词: Activity RECOGNITION MOTION Analysis STATISTICAL MOMENTS VIDEO INTERPRETATION
• 年，卷（期）: 2012,（1）
• 页码范围: 9-15
• 页数: 7页
• 分类号: TP39

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.