CogniPoint™ from PointGrab is an edge analytics solution for building automation, extracting the most important information about how and where occupants are using the facility. Comprised of highly intelligent image-based sensors and a cloud-based management system, it provides embedded-analytics for tracking human activity across the entire space of a building for actionable data.
The CogniPoint sensor is a miniature network connected sensing device, running deep learning and object tracking algorithms on an embedded ARM-based processor. The edge analytics solution is primarily designed for indoor occupant analytics and energy savings in commercial buildings, providing precision in the detection of occupants’ locations, count, and movements, as well as precise reading of ambient energy efficient lighting system and motion sensing.
Consistent with the edge computing approach, the CogniPoint sensor can work as a standalone device or be incorporated into other devices, such as thermostats or lighting fixtures. It is an intelligent building sensor that performs all analytics internally, so images are never stored or transmitted over the network, fully protecting occupant privacy. The product utilizes FIPS-140-2 compliant security functions; supports PoE, ZigBee, and Wi-Fi connectivity; and is powered by PoE, AC, or DC power.
Due to its processing capability and architecture, CogniPoint can also function as a sensor hub, controlling other sensors measuring temperature, humidity, and more. PointGrab’s management software package aggregates the data from the individual smart lighting sensors, monitors and maintains sensor operations, and delivers the information to various building management systems, including lighting, HVAC, safety, security, and facility management.
CogniPoint detects desks, meeting rooms, and occupancy in real time and provides precise space utilization data for hot desking and meeting rooms booking systems; provides data about occupants’ presence, count, positioning, and light distribution sensing, to enable significant energy savings in lighting and HVAC systems; provides a precise understanding of shoppers’ activity including traffic, paths, display attention, and dwell time to improve retail optimization; aids in the detection of a person who has fallen down, stopped moving for a long period of time, or is moving erratically, and can signal an alert; and offers cleaning and maintenance optimization.
