Towards Smart and Accessible Transportation Hub - Research Capacity Building and Community Engagement

Project Material

• Real-Time 3D Object Detection, Recognition and Presentation Using a Mobile Device for Assistive Navigation
• Improving Building Energy Efficiency through Data Analysis
• An AI-enabled Annotation Platform for Storefront Accessibility and Localization
• Context understanding in computer vision: A survey
• Exploring an Affective and Responsive Virtual Environment to Improve Remote Learning
• Real-time pedestrian pose estimation, tracking and localization for social distancing
• An Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings [An Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings]
• Precise indoor localization with 3D facility scan data
• MultiCLU: Multi-stage Context Learning and Utilization for Storefront Accessibility Detection and Evaluation
• ARMSAINTS: An AR-based Real-time Mobile System for Assistive Indoor Navigation with Target Segmentation
• SnapshotNet: Self-supervised feature learning for point cloud data segmentation using minimal labeled data
• Real-Time 3D Object Detection and Recognition using a Smartphone [Real-Time 3D Object Detection and Recognition using a Smartphone]
• A route optimization model based on building semantics, human factors, and user constraints to enable personalized travel in complex public facilities
• Impact of Labeling Schemes on Dense Crowd Counting Using Convolutional Neural Networks with Multiscale Upsampling
• Building an Annotated Damage Image Database to Support AI-Assisted Hurricane Impact Analysis
• Monocularly Generated 3D High Level Semantic Model by Integrating Deep Learning Models and Traditional Vision Techniques
• UnityPIC: Unity Point-Cloud Interactive Core
• ASSIST: Assistive Sensor Solutions for Independent and Safe Travel of Blind and Visually Impaired People
• A Snapshot-based Approach for Self-supervised Feature Learning and Weakly-supervised Classification on Point Cloud Data
• ASSIST: Evaluating the usability and performance of an indoor navigation assistant for blind and visually impaired people
• Multimodal Information Integration for Indoor Navigation Using a Smartphone
• SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management.
• Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling [Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling]
• Integrating AR and VR for Mobile Remote Collaboration
• Multi-level Scene Modeling and Matching for Smartphone-Based Indoor Localization
• Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks
• Generalizing semi-supervised generative adversarial networks to regression using feature contrasting
• Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
• Dense Crowd Counting Convolutional Neural Networks with Minimal Data using Semi-Supervised Dual-Goal Generative Adversarial Networks
• ASSIST: Personalized Indoor Navigation via Multimodal Sensors and High-Level Semantic Information
• Crowd Counting with Minimal Data Using Generative Adversarial Networks for Multiple Target Regression
• A Hybrid Indoor Positioning System for Blind and Visually Impaired Using Bluetooth and Google Tango
• Building Smart and Accessible Transportation Hubs with Internet of Things, Big Data Analytics, and Affective Computing