Human action representation using pyramid correlogram of oriented gradients on motion history images

The representation of human actions in video sequences is one of the key steps in action classification and recognition, performances of which are greatly dependent on the distinctiveness and robustness of the descriptors used for representation. In this paper, a novel descriptor, named pyramid correlogram of oriented gradients (PCOG), is presented for feature representation. PCOG, combined with the motion history images, captures both shape and spatial layout of the motion and therefore gives more effective and powerful representation for human actions and can be used for the detection and recognition of a variety of actions. Experiments on challenging action data sets show that PCOG performs significantly better than the histogram of oriented gradients both as a global descriptor and as a local descriptor.