Your Browser is Out of Date

Nytro.ai uses technology that works best in other browsers.
For a full experience use one of the browsers below
Computer Vision 3D Flow and Function AI with LiDAR
Introduction Solution overview Partner overview Architecture overview Sizing and scaling Use case validation
Use case validation
Smart Places
Multiple LiDAR sensors at an intersection Object detection LiDAR images and maps integration
Security
Create events for intrusion detection Capture person height and location Showcase tag and follow capabilities Third-party integration
Conclusions References

Security

Home > Workload Solutions > Computer Vision > White Papers > Computer Vision 3D Flow and Function AI with LiDAR > Security

Security

  • Create events for intrusion detection

    Intrusion detection can be a particularly important use case. To simulate this scenario, a zone was configured within the scene that people were not allowed enter.

    Results

    As pedestrians entered the restricted zone events were triggered that a person with a specific nonprivate ID entered the zone.

    Figure 5. Intrusion detection example
    Intrusion detection example

    Findings

    • Intrusion detection worked very well.
    • Various nonprivate metadata was also captured about the intruder (speed, location, and such).
    • It is not possible to identify an exact identity of the person since LiDAR does not capture facial details in the same way that a camera would. This is potentially advantageous depending on the specific privacy requirements associated with the monitored space.