A novel gradient based method for RGB-D human action recognition. Issue 4 (December 2018) - Tang, Haohao; Zhang, Hanling
Abstract: Most action recognition approaches proposed over the years that were designed for RGB sequences cannot utilize the rich 3D-structural information to reduce large intra-class variations. This paper addresses the issue of handling sole depth structural information for accurate human action recognition. It presents and evaluates the saliency based P-DmHOG features for human action representation. Saliency based P-DmHOG features are depth features inspired by the well-known HOG descriptor. Good results namely 96.78%, 97.78% and 93.13% are achieved on three public benchmark datasets: MSR Actions 3D, MSR Action Pairs 3D, and MSR Daily Activity 3D, which show the efficiency of proposed method to spatio-temporal and shape information.