After identifying moving object information with primary motion selection technology, separation of motion area among various images and secondary event selection technology detects events based on rules, and supports the quick decision-making of users.
The screen UI is designed by arranging rows according to emergency situations enabling rapid recognition.
The Escalator Abnormal Behavior Detection System detects escalator accidents and signs in real time using deep learning technology. It is possible to detect falling accidents, walking/running, reverse direction, and sudden stoppage, and a wide angle of view is secured by using a fisheye lens camera, making it possible to monitor up to the center of the escalator with a single CCTV camera.Download
The Arm Waving Detection System notifies CCTV monitoring personnel when a person waves one or both arms to request help in a dangerous situation. The motion history image (MHI) conversion technique is an effective method for checking large motions such as requesting help as it summarizes motions in one contrast frame and saves them.Download
The Intoxicated Person Detection System detects persons under the influence of alcohol based on object detection and tracking algorithms after acquiring image data via CCTV. It checks forward, backward, left, right, and stopping movements based on CCTV images, and restricts the entrance of persons under the influence of alcohol using facial recognition and temperature check at the kiosk.Download