As AI becomes more broadly adopted across all industries, edge-based AI (which filters and processes data locally on a camera) is being increasingly incorporated into video surveillance to enable end-to-end AI technology. Today, most security cameras send the data they collect to servers to be analysed. However, with edge-based AI, the data is analysed by the camera first and subsequently sent to the server.
This reduces the burden of transferring and storing large amounts of data to a server, thereby increasing efficiency, saving time, and reducing server costs typically required to analyse data.
- Increase event monitoring accuracy by reducing false alarms
- Classify target objects with attributes
- BestShot for minimising storage and bandwidth requirements
False alarm reduction
The analytics can be configured to ignore video noise, waving trees, moving clouds and animals, all of which might normally be the cause of false alarms when standard motion detection technology or sensors are being used to detect activity. This ability to minimise time-wasting and costly false alarms means control room operators and security personnel are able to focus on responding to real incidents and emergencies.
Control room efficiency
The licence-free deep learning video analytics simultaneously detects and classifies various object types, including people, vehicles, faces and licence plates and is supported by Wisenet AI algorithms unique to Hanwha Techwin which are able to identify the attributes of objects or people, such as their age group, their gender or the colour of the clothing a person is wearing.
People counting for Occupancy Monitoring
Wisenet Occupancy Monitoring Application combines Wisenet P series AI camera with people counting. AI algorithms built into the AI cameras can be utilised to accurately count the number of people entering and leaving premises and the data can be displayed to provide managers with information on the number of people present.
Social Distancing Detection