no code implementations • 24 Feb 2024 • Xiangyu Gao, Youchen Luo, Ali Alansari, Yaping Sun
This paper introduces MMW-Carry, a system designed to predict the probability of individuals carrying various objects using millimeter-wave radar signals, complemented by camera input.
no code implementations • 30 Nov 2023 • Xiangyu Gao, Yaping Sun, Dongyu Wei, Xiaodong Xu, Hao Chen, Hao Yin, Shuguang Cui
In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers.
no code implementations • 4 Jul 2023 • Xiangyu Gao, Sumit Roy, Lyutianyang Zhang
Our proposed algorithm follows a three-step approach: a) preprocessing of back-scattered received radar signal for 4-dimensional (4D) point clouds generation, b) 3-dimensional (3D) radar ego-motion estimation, and c) notch filter-based background removal in the azimuth-elevation-Doppler domain.
1 code implementation • 16 Jun 2023 • Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, Shaoting Zhang
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
no code implementations • 31 Oct 2021 • Xiangyu Gao, Hui Liu, Sumit Roy, Guanbin Xing, Ali Alansari, Youchen Luo
Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security.
no code implementations • 4 Apr 2021 • Xiangyu Gao, Sumit Roy, Guanbin Xing, Sian Jin
Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent of environmental conditions.
no code implementations • 22 Jan 2021 • Xiangyu Gao, Sumit Roy, Guanbin Xing
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features.
3 code implementations • 13 Nov 2020 • Xiangyu Gao, Guanbin Xing, Sumit Roy, Hui Liu
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception.
no code implementations • 20 Aug 2020 • Jinhe Shi, Xiangyu Gao, Chenyu Ha, Yage Wang, Guodong Gao, Yi Chen
Adverse drug events (ADEs) are a serious health problem that can be life-threatening.
1 code implementation • 3 Mar 2020 • Yizhou Wang, Zhongyu Jiang, Xiangyu Gao, Jenq-Neng Hwang, Guanbin Xing, Hui Liu
Radar is usually more robust than the camera in severe driving scenarios, e. g., weak/strong lighting and bad weather.
1 code implementation • 29 Dec 2019 • Xiangyu Gao, Guanbin Xing, Sumit Roy, Hui Liu
Millimeter-wave (mmW) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) for its ability to provide high accuracy location, velocity, and angle estimates of objects, largely independent of environmental conditions.