  Identifying human behaviors is a challenging research problem due to the
complexity and variation of appearances and postures, the variation of camera
settings, and view angles. In this paper, we try to address the problem of
human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of
temporal segments. Then for each segment, we compute dense optical flow, which
provides instantaneous velocity information for all the pixels. We then compute
Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a
descriptor vector of 192- dimensions. We then train a non-linear multi-class
SVM that classify different human behaviors with the accuracy of 72.1%. We
evaluate our method by using publicly available human action data set.
Experimental results shows that our proposed method out performs state of the
art methods.
