A5058607846- Hand Gesture Recognition Systems
- Gait Recognition and Analysis
- Robot Manipulation and Learning
- Advanced Neural Network Applications
- Muscle activation and electromyography studies
- COVID-19 diagnosis using AI
- Advanced Data and IoT Technologies
- Human Pose and Action Recognition
- Adversarial Robustness in Machine Learning
- Advanced Optical Sensing Technologies
- Evacuation and Crowd Dynamics
- Gaze Tracking and Assistive Technology
Wuhan University of Science and Technology
2020-2023
There are a variety of objects, random postures and multiple objects stacked in disorganized manner unstructured home applications, which leads to the object grasping posture estimation planning based on machine vision become very complicated. This paper proposes method cluttering pose detection convolutional neural network with self-powered sensors information. Firstly, search strategy for candidate poses 3D point cloud is proposed, single-channel image dataset representing this established...
With the rapid development of sensor technology and artificial intelligence, video gesture recognition under background big data makes human–computer interaction more natural flexible, bringing richer interactive experience to teaching, on‐board control, electronic games etc. To perform robust conditions illumination change, clutter, movement, partial occlusion, an algorithm based on multi‐level feature fusion two‐stream convolutional neural network is proposed, which includes three main...
Abstract Robot grasping technology is a hot spot in robotics research. In relatively fixed industrialized scenarios, using robots to perform grabbing tasks efficient and lasts long time. However, an unstructured environment, the items are diverse, placement posture random, multiple objects stacked occluded each other, which makes it difficult for robot recognize target when grasped grasp method complicated. Therefore, we propose accurate, real‐time detection based on convolutional neural...
Abstract With the rapid development of sensor technology and artificial intelligence, video gesture recognition under background big data makes human‐computer interaction more natural flexible, bringing richer interactive experience to teaching, on‐board control, electronic games, etc. In order perform robust conditions illumination change, clutter, movement, partial occlusion, an algorithm based on multi‐level feature fusion two‐stream convolutional neural network is proposed, which...
Summary As the development of deep learning and continuous improvement computing power, as well needs social production, target detection has become a research hotspot in recent years. However, algorithm problem that it is more sensitive to large targets does not consider feature‐feature interrelationship, which leads high false or missed rate small targets. An method (C‐SSD) based on improved SSD proposed, replaces backbone network VGG‐16 with dense convolution (C‐DenseNet) achieves further...