Object Recognition using various sensors

Humans recognize objects from visible light, but with the recent development of sensor technology, sensors can now detect light amounts that humans cannot detect and invisible light such as infrared rays.

We aim at monitoring elderly people by efficiently using information from such sensors.

Person Recognition focusing on Behavior

In order to reduce the number of accidents in which visually impaired people fall off from station platforms, we are conducting researches to recognize whether they are visually impaired or not from the surveillance camera image, using the white cane, movement and pose of the user.

When searching for a specific person from surveillance camera video, it takes a lot of time and effort to do so manually

Therefore, we also conduct researches to automatically find the target person from long and numerous surveillance camera videos.

Semantic Segmentation

In automated driving systems, it is necessary to accurately recognize the surrounding environment. For this purpose, “semantic segmentation” is used, which allocates a label of attribute such as “cars” and “pedestrians” to each pixel in an image taken with cameras on board vehicles.

In our Lab, we are applying this to researches that further divide the attributes of “pedestrians”, researches that automatically acquires runways, and researches that performs semantic segmentation to facilities along the railway.

Detectability estimation

For safer driving, driving assistance systems that assist safe driving by informing drivers about pedestrians and signs recognized by cars are being put into practical use. However, informing all pedestrians and signs to the driver is inefficient and may be discomfortable.

Therefore, we are researching a method to convey only difficult-to-see objects to the driver by estimating the visibility of pedestrians and signs from the driver's viewpoint.