Structured Images for RGB-D Action Recognition

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RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “[156] proposed to represent a depth map sequence into three pairs of structured dynamic images at body, part and joint levels respectively through bidirectional rank pooling.”
Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018, methodology): “Wang et al. [42] and Hou et al. [43] proposed the concept of structured images for depth based action recognition, but the proposed methods need the help of skeleton data to locate the position of human body, parts and joints.”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “the other methods [9], [56], [217] focused on using depth videos captured by active sensors to recognize actions, due to the availability of low-cost and reliable active sensors like Kinect.”
Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition (2018, unknown): “This paper is an extension of [40].”