National University of Defense Technology

Yingmei Wei

Jie Jiang

Liang Bai

Lai Kang

Yuxiang Xie

Jian Li

Yang Liu

Yulun Wu

Yixiang He

Xing Xin

Yujie Fang

Songyang Lao

Yin Zou

Yanming Guo

Jiahao Sun

Xianghan Wang

Junyan Yang

The disclosure relates to a method and a system for hand pose recognition, a device and a storage medium are disclosed in embodiments of the disclosure. The method includes: capturing a RGB image of a hand from a RGB camera and capturing a depth image of the hand from an active depth camera, so as to obtain a hand pose data set according to the RGB image and the depth image; processing the hand pose data set to obtain a 3D joint position, and taking the 3D joint position as a data set for training a software model; extracting the RGB image by a feature extractor based on a depth neural network to obtain a feature map of a hand pose; and processing the feature map according to an attention mechanism to obtain a global feature map of the hand pose.

Method and system for hand pose recognition, device and storage medium

A training method for a robust neural network based on feature matching is provided in this disclosure, which includes following steps. Step A, a first stage model is initialized. The first stage model includes a backbone network, a feature matching module and a fullple loss function. Step B, the first stage model is trained by using original training data to obtain a second stage model. Step C, the second stage model is attacked so as to generate PGD adversarial samples of the original training data, and the second stage model is trained again with the generated adversarial samples and the original training data. Step D, training parameters are adjusted and the second stage model is trained again, and parameters for which the model has highest accuracy on an original test set are saved.

Training method for robust neural network based on feature matching

The present disclosure provides a visual localization method and apparatus based on a semantic error image. The method includes: performing feature extraction for a target image, and obtaining at least one matching pair by performing feature matching for each extracted feature point and each three-dimensional point of a constructed three-dimensional scene model; obtaining a two-dimensional semantic image of the target image by performing semantic segmentation for the target image; and determining semantic information of each matching pair according to semantic information of each pixel of the two-dimensional semantic image; constructing a hypothesized pose pool including at least one hypothesized pose according to at least one matching pair; for each hypothesized pose, constructing a reprojection error image and a semantic error image; determining a hypothesized pose with a minimum reprojection error and a minimum semantic error as a pose estimation according to the reprojection error image and the semantic error image of each hypothesized pose. Optimal pose screening is performed using the semantic error image constructed based on a semantic error, so as to achieve good localization effect even in a case of significant change of a scene.

Visual localization method and apparatus based on semantic error image

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Hunan, China

Yingmei Wei