Human action recognition using direction histograms of optical flow

Recognizing human actions is a challenging research area due to the complexity and variation of human's appearances and postures, the variation of camera settings, and angles. In this paper, we introduce a motion descriptor based on direction of optical flow for human action recognition. The directional value of a silhouette is divided into small regions. In each region, the normalized direction histogram of optical flow is computed. The motion vector is the values of a histogram in every region respective concatenation. The vectors are smoothed in time domain by moving average to reduce the motion variation and noise. For the training process, the motion vectors of the training set are clustered by K-mean to represent action. The clustered data group the similar posture together and is represented by the cluster centers. The centers are used to compare input frames by computing distance and using K-nearest neighbor to classify action. The experimental results show that K-mean clustering can group the similar pose together. The motion feature can be used to classify action in a low resolution image with a small number of reference vectors.