Classification of Holoscopic 3D Micro-Gesture Images and Videos

摘要

This paper presents an empirical study on applying convolutional neural networks (CNNs) to the Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018[1]). Based on some neural networks trained on large scale datasets such as ImageNet, we are able to get fine-tuned models to work well on the HoMGR 2018 data. Result shows that resolution of inputs is critical for model accuracy. On test sets, the accuracy is 0.867 for frames based challenge and 0.82 for videos based challenge.