Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks

TL;DR

This paper introduces a deep neural network-based system for classifying and visualizing motion capture sequences using simple features, achieving state-of-the-art accuracy on a large dataset.

Contribution

It presents a novel approach combining simple features with deep neural networks for large-scale gesture recognition, improving accuracy and interpretability.

Findings

Achieved over 95% classification accuracy on 65 classes

Deep neural networks outperform PCA in feature discrimination

Effective visualization of learned features using deep autoencoders

Abstract

The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative positions of joints (PO), temporal differences (TD), and normalized trajectories of motion (NT). Given these features a hybrid multi-layer perceptron is trained, which simultaneously classifies and reconstructs input data. We use deep autoencoder to visualize learnt features, and the experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can. We test our system on a public database with 65 classes and more than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our knowledge, the state of the art result for such a large dataset.