3D Convolutional Network based micro-gesture recognition
Congyue Zhang, Wenjie Fu, Canrong Tian, Xu Cheng, Yuan Tian, Hao Yu · 2024
Micro action recognition is an important research area in human motion analysis, which can be applied to the fields of healthcare, motion analysis and human-computer interaction. In this paper, we propose a 3D convolutional neural network-based micro-gesture recognition network to improve the performance, which combines skeletal and semantic embedding losses to enhance network discrimination. Specifically, first, various data enhancement techniques, such as level flipping and random Gaussian noise, are utilized to improve the robustness and generalization of the model. Horizontal flipping aims to help the model better recognise mirrored and stacked features in images by flipping them on the symmetry axis. Meanwhile, Gaussian noise, also known as white noise, is a set of random values with a normal distribution designed to force the model to learn features that are robust to small variations in the input, which can represent smudges or subtle absences in the image. We have evaluated our methods on a micro-posture recognition benchmark dataset, and these methods improve on previous methods. The model was tested on the iMiGUE dataset and achieved a Top1% accuracy of 59.01%—a notable improvement over other models.