DeepSegment: Segmentation of motion capture data using deep convolutional neural network

作者：

Highlights：

• We propose a novel data-driven approach for the segmentation of motion capture data.

• We encode x, y, and z coordinates of a joint into an RGB image pixel. In this way, we develop an image that represents the complete motion.

• We adopt a transfer learning approach and use a pre-trained deep CNN architecture, “Alexnet” to extract the fixed-size features.

• We build Kd-tree on these fixed-size features to search and retrieve nearest neighbors from the input query frames.

• Based on the retrieved nearest neighbors, the actions are classified, and the segmentation is performed.

摘要

•We propose a novel data-driven approach for the segmentation of motion capture data.•We encode x, y, and z coordinates of a joint into an RGB image pixel. In this way, we develop an image that represents the complete motion.•We adopt a transfer learning approach and use a pre-trained deep CNN architecture, “Alexnet” to extract the fixed-size features.•We build Kd-tree on these fixed-size features to search and retrieve nearest neighbors from the input query frames.•Based on the retrieved nearest neighbors, the actions are classified, and the segmentation is performed.

论文关键词：Motion capture data,Segementation,Convolutional neural network,Alexnet,Kd-tree

论文评审过程：Received 4 September 2020, Revised 11 February 2021, Accepted 15 February 2021, Available online 28 February 2021, Version of Record 15 March 2021.

论文官网地址：https://doi.org/10.1016/j.imavis.2021.104147