Person Using Firearms Detection
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Nodeflux Person Using Firearms Detection addresses the use of firearms by a person and monitors the emergence of shooting event, promoting improved public security and surveillance within monitored environments. This solution integrates advanced Large Vision Models and Visual Transformers directly into the Nodeflux Visionaire platform. The analytics system is capable of automatically detecting individuals using firearms in real-time scenarios.
Nodeflux Person using Firearms Detection is primarily intended for use in law enforcement and surveillance applications. It monitors the occurrence of individuals using firearms and aids in mitigating or even preventing potential risks or threats associated with shooting events that may be impacting in mortality cases.
Disclaimer: In a sense of large vision models and visual transformers technology, the performance of this analytic might slightly differ in your environment, depending on several variables such as camera specs, camera height, camera angle, weather conditions, etc. We highly recommend you to test our analytics and run benchmarks on your own images, with ground truth or your quality expectations prepared beforehand. Please contact us for more info.
Visionaire Person using Firearms Detection utilizes a combination of other services:
Postgres - For database,
Docker Snapshot - For object detection related,
Visionaire Docker Stream (must be v4.57.11 and above) - For video stream processing,
Visionaire Dashboard (Optional) - Built-in dashboard for visualization.
Since Nodeflux Person Using Firearms Detection utilizes the snapshot platform, an additional docker-compose.yml file needs to be deployed alongside the other aforementioned services. The following directive link outlines the installation process for deploying the docker-compose.yml file specific to the Open Vocabulary Object Detection (OVOD) pipeline of snapshot platform:
When you are assigning this analytics into the stream through the dashboard, you should configure some parameters setting below:
dump_interval
How frequent you want the stream as a frame snapshot to get the inference result. Unit in second
address
The IP address and its port where the OVOD snapshot service is deployed
dump_confidence
The value of minimum confidence threshold so that the inference result should be dumped as the event. Unit ranged between 0.1 to 1