Urban Spaces & Infrastructure

Setting the timetable for the Toei Subway requires knowing accurately which station, which car, and how many people are boarding and alighting. But surveys were carried out by staff observing in person, which limited the places, times and cars that could be measured.

Edge AI cameras on the platform measure the number of passengers inside the car and the numbers boarding and alighting while it is stopped. Because footage is never sent to the cloud and only counts are extracted on the spot, the setup can stay in place in a public space. Site-specific conditions — such as UV-blocking window film affecting analysis accuracy — were established by installing and testing rather than assumed.

To make smart buildings widespread. The obstacle was that each piece of equipment required its own development and operation, leaving the burden of managing edge devices customer by customer and building by building.

The “DX-Core” building OS and Actcast are integrated platform to platform, shifting edge device management onto Actcast. That removes the need to manage large numbers of devices separately in every building, and lets the integration itself be assembled faster and in a better shape. Actcast already had a track record of remotely operating AI cameras dispersed across large spaces such as commercial facilities, and that operational capability carries straight over to buildings.

In a large commercial complex, there was no accurate, real-time picture of how visitors moved through the building. Capturing entries and exits per floor would make time-of-day congestion and imbalance between floors visible, and give initiatives something to be judged against.

Within a framework that controls and manages cameras across the whole area centrally, we handle the AI image analysis. Being able to add analysis while keeping the existing camera network in place is a precondition at this scale. What is captured is statistics on counts and movement — the setup does not record individuals.