AI Analytics Solutions

Introduction

VENZO Secure Analytics Solutions Powered by SAIMOS is a flexible and scalable solution based on Deep Learning and applicable to a host of verticals and use cases.

The analytics can run independantly, as a standalone product or together with the Milestone XProtect VMS where the award-winning integration enables easy configuration and seamless Smart Client usage.

Our comprehensive video analytics platform meets diverse needs across multiple verticals.

Perimeter Protection
Object Detection
Mask Detection
Loitering
Dynamic Blurring
2D & 3D Counting
Heatmapping
3D Tailgating Detection & Gate access
Observation
Face Recognition
Number plate detection

Intelligent Perimeter Protection

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 false alarm rates. Furthermore, we 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. With configurable parameters such as speed, direction and retention time.

A specialised filter algorithm (based on artificial intelligence) supports the avoidance of unwanted alarms. For improved accuracy then special conditions of a certain protected zone can be trained by machine learning to improve the efficiency of perimeter protection.

Object recognition for perimeter- and zone protection is primarily implemented with novel tracking by detection systems based on neural networks.

Several object classes can be tracked simultaneously, and a holistic situational awareness is provided for the observed scene.

Object Detection

Protects indoor areas against theft
Detects unwanted objects (e.g. blocked emergency exits)
Detects long term loitering
Detects left- and theft objects
Simple configuration by defining the minimum and maximum object size to be detected