# A New Representation of Skeleton Sequences for 3D Action Recognition

## TL;DR

This paper introduces a novel representation of skeleton sequences for 3D action recognition, transforming sequences into clips based on cylindrical coordinates and employing deep neural networks to learn spatial and temporal features.

## Contribution

The paper proposes a new skeleton sequence representation using cylindrical coordinate-based clips and a multi-task learning framework for improved 3D action recognition.

## Key findings

- Effective representation of skeleton sequences for action recognition
- Deep neural networks successfully learn spatial and temporal features
- Significant improvement over existing methods

## Abstract

This paper presents a new method for 3D action recognition with skeleton sequences (i.e., 3D trajectories of human skeleton joints). The proposed method first transforms each skeleton sequence into three clips each consisting of several frames for spatial temporal feature learning using deep neural networks. Each clip is generated from one channel of the cylindrical coordinates of the skeleton sequence. Each frame of the generated clips represents the temporal information of the entire skeleton sequence, and incorporates one particular spatial relationship between the joints. The entire clips include multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We propose to use deep convolutional neural networks to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and then use a Multi-Task Learning Network (MTLN) to jointly process all frames of the generated clips in parallel to incorporate spatial structural information for action recognition. Experimental results clearly show the effectiveness of the proposed new representation and feature learning method for 3D action recognition.