ATM Burglary Incident Recognition

ATM Burglary Incident Detection

Nodeflux ATM Burglary Incident Detection

Nodeflux ATM Burglary Incident Detection addresses the action recognition instances of crime attempt and monitors the emergence of ATM burglary incident, 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 attempting to doing ATM burglary, through the inference process from multiple sequential frames, in real-time scenarios.

Designed Applications

Nodeflux ATM Burglary Incident Detection is primarily intended for use in law enforcement and surveillance applications. It monitors the occurrence of individuals attempting ATM burglary and aids in mitigating or even preventing potential risks or threats associated with crime events that may be impacting in mortality cases and financial losses

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.

Architecture

Visionaire ATM Burglary Incident Detection utilizes a combination of other services:

1. Postgres - For database,

2. Docker Snapshot - For action recognition related,

3. Visionaire Docker Stream (must be v4.57.11 and above) - For video stream processing,

4. Visionaire Dashboard (Optional) - Built-in dashboard for visualization.

Additional Necessary Deployment (VM)

Since Nodeflux ATM Burglary Incident 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 Large Visual Language Model (VM) pipeline of snapshot platform:

VM Installation

Analytics Configuration

When you are assigning this analytics into the stream through the dashboard, you should configure some parameters setting below:

{
  "address": "http://192.168.101.105:4008", 
  "images_num": 4, //default value is 2
  "dump_interval": 1, //default value is 2
  "interval_capture": 0.5 //default value is 1
}

Parameter	Explanation
address	The IP address and its port where the VM snapshot service is deployed
images_num	Number of sequential frames/snapshots that generated to be analyze in the inference process. Value range between 2 to 6 images
dump_interval	How frequent you want the stream as the multiple sequential frames/snapshots to get the inference result. Unit in second
interval_capture	Interval time between 2 frames/snapshots within the same sequential frames/snapshot. Unit in second