Video Spotlight: Dr. Zhu's Smart Transportation Hub - GCTC Expo Video
Dr. Zhu and his team started this project as a DHS Summer Research Team. This video exhibits the technologies under development by the Smart Transportation Hub Action Cluster in the NIST Grand City Team Challenge. These technologies are designed to assist those with additional challenges, improve security, and boost transportation efficiency.
In order, during the video we see:
- 3D Model: A3 D point cloud model of the entrance of a facility the technologies are deployed at. The 3D model was made in just a half a day using the advanced 360-degree LiDAR scanner owned by the DHS Center for Visualization and Data Analytics at Rutgers.
- Beacon Localization: The use of Bluetooth beacons to localize the user.
- App Navigation: Step by step navigation on the user’s mobile device utilizing the beacons, 2D/3D maps, and image analysis.
- 3D Model: Another 3D point cloud moving toward the elevators of the building.
- 2D/3D Matching and Segmentation: Using an image taken by a mobile device to localize the user within the3D model and determine important nearby features (rooms, elevators, stairs, etc.)
- Crowd Analysis: Automatic crowd detection and analysis using a state-of-the-art deep learning approach, convolutional neural network (CNN) for navigation and security purposes.