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

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.