Characterizing Human Behaviours Using Statistical Motion Descriptor
This work addresses the problem of identifying human behaviors from video for applications like surveillance or human-computer interaction, but it appears incremental as it builds on existing optical flow and SVM techniques.
The paper tackles human behavior identification by introducing a novel statistical motion descriptor based on Histogram of Optical Flow, achieving an accuracy of 72.1% on a public dataset and outperforming state-of-the-art methods.
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.