XRTS-100
Using visual AI to detect people in CCTV footage and replicate any abnormal situations
(such as loitering, falls, or accidents) in real-time within a digital twin environment
This solution not only helps prevent safety incidents but also facilitates rapid follow-up actions
Virtual Fence
Counting of moving objects
Intrusion Detection
Detection of objects that have entered a restricted area
Wandering Detection
Detecting movement of objects within restricted areas
Privacy Protection
Representing individuals as skeletons or avatars to preserve personal anonymity
Falliang Detection
Identifying situations where objects fall within the surveillance area
Pass Out Detection
Detecting instances where a person has fallen
Crush Injuries Detection
Recognizing scenarios where a body is caught in machinery
Facial Recognition
Managing access using facial information
Monitoring and tracking locations
Expected Effects
- Immediate alerts are sent when abnormal situations occur, allowing for a prompt response.
- Detection alerts provide confirmation of incident signs as soon as they happen.
- Real-time monitoring enables understanding of the building’s interior situation, even in the absence of personnel.
- Effective management of large buildings is achievable with limited staff, ensuring cost-effectiveness.
- Measures are taken to prevent and handle safety accidents related to high-risk facilities.
Application Field
- Buildings and facilities that need access prohibition and danger zone management.
- Construction sites, manufacturing factories, and other locations where people might fall or have accidents.
- Small business owners who aim to prevent crimes during unmanned hours through access and patrol detection, as well as reduce accidents caused by falls.
- Industries such as manufacturing and construction, where safety accidents occur due to high-risk equipment.
VECTORSIS Inc.
CEO : Taehoon KANG
Tel : 031-272-3360
Email : support@vectorsis.com
주소: 401, Glocal Industry-Academic Cooperation Building, 152 Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do (Dankook Univ., jukjeon-dong)
Business Registration Number: 885-87-00552