Grekkom
Grekkom - Version Perimeter Lockout AI -Flexible And Scalable Video-Analytics Solution
FromGrekkom
Perimeter Lockout AI video analytics algorithms are based on artificial intelligence and are mainly used in professional environments. Perimeter Lockout AI modules are characterized by highest stability, accuracy &quality, easy configuration, VMS integration (Milestone, SeeTec and others) as well as excellent support.
Most popular related searches
Detects
- Intrusion
- Prowling
- Crawling
- Dropped / removed object
- Frontier crossing
- Stopped vehicle
- Vehicle driving against traffic
Applies to
- Critical infrastructures
- Photovoltaic power stations
- Prison surveillance
- Frontier/ border control
- Residential areas surveillance
- Tunnels
- Railway surveillance
- Automatic camera detection.
- Easy scene calibration based on body size directly in the live video view.
- Optimized for 24/7 real-time operation.
- Milestone XProtect integration.
- Minimal pixel density: 12 pixel/meter.
- It does not generate false alarms due to vegetation.
- It does not generate false alarms for animals.
- It does not generate false alarms due to changes in lighting.
- It does not generate false alarms due to drops on rainy days.
- Outdoor & Indoor zones, buildings and premises, in general, are reliably secured with our intelligent perimeter protection.
- Early alarms are triggered by unwanted intruders detected with our easy to configure perimeter protection.
- Our algorithms are very stable with excellent low false alarm rates. Furthermore, we do support various scenarios for proximity detection ranging from crawling to sprint.
- The configuration of our perimeter protection is very user friendly to configure and offers a simple zone system. Of course, we do allow the combination of several parameters like speed, direction and retention time.
- A specialized filter algorithm (based on artificial intelligence) supports the avoidance of unwanted alarms. For improved accuracy, special conditions of a certain protected zone can be trained by machine learning to improve efficiency in perimeter protection.
- Object recognition for perimeter and zone protection is primarily implemented with novel tracking by detection systems based on neural networks. A classifier algorithm is applied to the respective scene enabling the distinction between different objects.