Artificial Intelligence

Most of the existing human action datasets are common human actions in daily scenes（e.g. NTU RGB+D series, Kinetics series）, not created for Human-Robot Interaction(HRI), and most of them are not collected based on the perspective of the service robot, which can not meet the needs of vision-based interactive action recognition.

This dataset is named as “Human-Robot Interactive Action Dataset From The Perspective Of Service Robot (THU-HRIA dataset) “ ,which is created for indoor service robot action interaction, collected by Tsinghua University.

The dataset includes channel frequency response (CFR) data collected through an IEEE 802.11ax device for human activity recognition. This is the first dataset for Wi-Fi sensing with the IEEE 802.11ax standard which is the most updated Wi-Fi version available in commercial devices. The dataset has been collected within a single environment considering a single person as the purpose of the study was to evaluate the impact of communication parameters on the performance of sensing algorithms.

The rocket nose-cone shapes have been generated by blending few conic sections together (two conic sections in one) and the simulated against mach number regime from subsonic through transonic to supersonic. The aerodynamic drag coefficients have been recoded for each shape for each mach number.

Open set face recognition on a small dataset, in terms of the amount of image samples per individual, is a hard and active area of study. This study investigates the open set face verification and face identification problems on the IFPLD dataset, which consists of only one frontal and one profile image per individual. 

This dataset contains trained weights to predict ECG abnormalities.

This folder consists of codes, dataset, and models.

Perth-WA is the localization dataset that provides 6DoF annotations in 3D point cloud maps. The data comprises a LiDAR map of 4km square region of Perth Central Business District (CBD) in Western Australia. The scenes contain commercial structures, residential areas, food streets, complex routes, and hospital building etc.

The ability to perceive human facial emotions is an essential feature of various multi-modal applications, especially in the intelligent human-computer interaction (HCI) area. In recent decades, considerable efforts have been put into researching automatic facial emotion recognition (FER). However, most of the existing FER methods only focus on either basic emotions such as the seven/eight categories (e.g., happiness, anger and surprise) or abstract dimensions (valence, arousal, etc.), while neglecting the fruitful nature of emotion statements.

Human activity recognition (HAR) has attracted much attention. However, the existing HARs have shortcomings, such as few recognized activities, no identification, privacy leakage, and battery maintenance. Aiming at these shortcomings, this team has devised a body RFID skeleton that fully senses human activity and further proposes highly-accurate and fine-grained (total of 21 activities) HARs. The body RFID skeleton senses human activity by collecting tag response records of the skeleton node.