40 citations · 85 across the 9 of their papers we have counts for
9 papers
CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception
Tam Bang, Hoang H. Nguyen, Lei Cheng +6
Reliable roadside perception of vulnerable road users (VRUs) remains challenging under occlusions, variable lighting, and diverse weather conditions, particularly under strict edge…
Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning
Pengxin Wang, Lihao Guo, Yi Xie +3
Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives. In this setting, conflicts a…
Radar-Camera Fused Multi-Object Tracking: Online Calibration and Common Feature
Lei Cheng, Siyang Cao
This paper presents a Multi-Object Tracking (MOT) framework that fuses radar and camera data to enhance tracking efficiency while minimizing manual interventions. Contrary to many…
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
Lei Cheng, Lihao Guo, Tianya Zhang +5
Accurate multi-sensor calibration is essential for deploying robust perception systems in applications such as autonomous driving and intelligent transportation. Existing LiDAR-cam…
TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection
Lei Cheng, Siyang Cao
Despite significant advancements in environment perception capabilities for autonomous driving and intelligent robotics, cameras and LiDARs remain notoriously unreliable in low-lig…
mmWave Radar for Sit-to-Stand Analysis: A Comparative Study with Wearables and Kinect
Shuting Hu, Peggy Ackun, Xiang Zhang +5
This study explores a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology. The goal is to develop a non-contact sensing, priva…
Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors
Lei Cheng, Arindam Sengupta, Siyang Cao
Autonomous driving holds great promise in addressing traffic safety concerns by leveraging artificial intelligence and sensor technology. Multi-Object Tracking plays a critical rol…
A Survey on Radar-Based Fall Detection
Shuting Hu, Siyang Cao, Nima Toosizadeh +3
Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern where timely detection can greatly minimize harm. With the advance…
Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera
Lei Cheng, Siyang Cao
Sensor fusion is essential for autonomous driving and autonomous robots, and radar-camera fusion systems have gained popularity due to their complementary sensing capabilities. How…