A Review of Dynamic Maps for 3D Human Motion Recognition Using ConvNets and Its Improvement

Abstract

RGB-D based action recognition is attracting more and more attention in both the research and industrial communities. However, due to the lack of training data, pre-training based methods are popular in this field. This paper presents a review of the concept of dynamic maps for RGB-D based human motion recognition using pretrained models in image domain. The dynamic maps recursively encode the spatial, temporal and structural information contained in the video sequence into dynamic motion images simultaneously. They enable the usage of Convolutional Neural Network and its pretained models on ImageNet for 3D human motion recognition. This simple, compact and effective representation achieves state-of-the-art results on various gesture/action/activities recognition datasets. Based on the review of previous methods using this concept upon different modalities (depth, skeleton or RGB-D data), a novel encoding scheme is developed and presented in this paper. The improved method generates effective flow-guided dynamic maps, and they could select the high motion window and distinguish the order among the frames with small motion. The improved flow-guided dynamic maps achieve state-of-the-art results on the large Chalearn LAP IsoGD and NTU RGB+D datasets.