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N. Olivera, A. Garg and E. Horvitz, “Layered Representations for Learning and Inferring Office Activity from Multiple Sensory Channels,” Computer Vision and Image Understanding, Vol. 96, No. 2, 2004, pp. 163-180. doi:10.1016/j.cviu.2004.02.004

has been cited by the following article:

• TITLE: A Fast Statistical Approach for Human Activity Recognition

  AUTHORS: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

  KEYWORDS: Activity Recognition; Motion Analysis; Statistical Moments; Video Interpretation

  JOURNAL NAME: International Journal of Intelligence Science, Vol.2 No.1, January 14, 2012

  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.