Retracted: Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

Abstract

The purpose of this article is to identify football and related environmental variables through its VR images under the current situation where the vision system has become the only way for football robots to obtain the external environment, so as to improve the chances of winning the game. First of all, this article uses color filtering to enhance the football characteristics in the VR perspective, so as to distinguish the football from the off-field environment and shadows. The optimal threshold of the enhanced image is automatically determined by the Ostu method, so the image will not be affected by shadows during segmentation, and the contour of the image can be segmented. At the same time, using the humanoid medium-sized football game machine system as the platform, the relevant processing algorithms of the humanoid football robot front-view system are studied to realize the work of color image segmentation, edge extraction, straight line extraction, cross-line recognition and target post-recognition. PA-SIFT algorithm is used to quickly identify the graphics. Data verification results show that the recognition rate of the PA-SIFT algorithm can reach 96%, ensuring the real-time and feasibility of the algorithm. In addition, the divide-and-conquer algorithm and the related processing algorithm of the vision system are combined to determine the central area of the image, so that the algorithm is not affected by the external environment, and the algorithm is robust and can improve actual competition.

Soccer’s AI transformation: deep learning’s analysis of soccer’s pandemic research evolution

“…Similarly, we found studies from both periods regarding the development of individual technologies such as image processing and humanoids to improve the AI performance of soccer robots (Bai et al, 2020;Hu et al, 2021;Janos et al, 2022). The soccer robot is more of a source technology for maximizing robot and AI technology than a 10.3389/fpsyg.2023.1244404…”

A Hybrid Image Augmentation Technique for User- and Environment-Independent Hand Gesture Recognition Based on Deep Learning

“…The advancement of gesture recognition systems significantly enhances the interaction between computers and humans, with hand gestures becoming increasingly prevalent across various sectors. Hand gestures are now used in diverse applications such as gaming [17,18], virtual reality and augmented reality [19,20], assisted living [21,22], and more. Moreover, the recent proliferation of hand gesture recognition in industries like human-robot interaction in manufacturing [23,24] and autonomous vehicle control [25] has spurred considerable interest.…”

Investigating Effective Geometric Transformation for Image Augmentation to Improve Static Hand Gestures with a Pre-Trained Convolutional Neural Network

“…As a result, much research has focused on developing more accurate and effective hand gesture recognition (HGR) methods and applying them in various contexts. Hand gestures are currently utilized in multiple applications, such as games [1,2], virtual and augmented reality [3][4][5], assisted living [6,7], and cognitive development evaluation [8]. In addition, hand gesture recognition has gained significant interest in several industries, including human-robot interaction in manufacturing [9][10][11] and autonomous vehicle control [12,13].…”