Human Action Recognition from 3D Landmark Points of the Performer

Abstract

Recognizing human actions is an active research area, where pose of the performer is an important cue for recognition. However, applying the 3D landmark points of the performer in recognizing action, is relatively less explored area of research due to the challenge involved in the process of extracting 3D landmark points from single view of the performers. With the recent advancements in the area of 3D landmark point detection, exploiting the landmark points in recognizing human action, is a good idea. We propose a technique for Human Action Recognition by learning the 3D landmark points of human pose, obtained from single image. We apply an autoencoder architecture followed by a regression layer to estimate the pose parameters like shape, gesture and camera position, which are later mapped to the 3D landmark points by Skinned Multi Person Linear Model (SMPL model). The proposed method is a novel attempt to apply a CNN based 3D pose reconstruction model (autoencoder) for recognizing action. Further, instead of using the autoencoder as a classifier to classify to 3D poses, we replace the decoder part by a regressor to obtain the landmark points, which are then fed into a classifier. The 3D landmark points of the human performer(s) at each frame, are fed into a neural network classifier as features for recognizing action.