Our research uses the data which can be acquired from the real world, such as:
- Human activity data (acceleration, angular velocity, etc.)
- Image (RGB, thermal, etc.)
- Laser point cloud
- Operation data of public transportation
We have 3 research groups.
Context estimation team researches on the recognition of human activity and the estimation of the surrounding environment from various data. We use the data which can be collected by inertial sensors of smartphones, signal strength of Bluetooth devices, magnetic strength etc.
For example, human activity recognition aims accurate recognition of our daily activity such as walking, sitting down, climbing the stairs. Another example is location estimation indoors. We cannot use GPS in the building and underground areas. To make our devices provide the suitable information based on the situation which the users are facing.
3D information processing team researches on "artificial intelligence which can understand the environment." We use RGB image, thermal image, and other sensor data to realize automatic re-creation of space map and recognition of scene context and so on. We are doing 3D modeling, change detection, scene recognition etc. using core methods such as image processing, deep learning.
Super-Smart society team researches on data analysis on transportation data, human flow, etc. to realize highly efficient society. Research topics are: delay estimation using bus operation data, socoiety-wide infrastructure system with demand-supply concept.
THis project is related to 3 teams above. This project aims a new way of logistics and people flow in the world where autonomous vehicles run everywehere. Topics are environment recognition using autonomous vehicle, large scale (city level) people flow simulation, etc.
HASC (Human Activity Sensing Consortium) is working to collect and to distribute large-scale human activity corpus.