Road Crash Monitoring

Road Crash Monitoring by Detecting Vehicle Crash

Nodeflux Road Crash Monitoring System

The Nodeflux Road Crash Monitoring System empowers users to proactively mitigate traffic incidents by identifying the presence of events or post-events after the incident occured. This solution leverages advanced Large Vision Models and Visual Transformers integrated directly within the Nodeflux Visionaire platform. The analytics system automatically detects road conditions, analyzing them in real-time to determined whether a vehicle crash or not based on real-time inferences.

Designed Applications

Nodeflux Road CrashMonitoring is primarily intended for use in public road surveillance applications. It functions by monitoring for the occurrence of road traffic incident through vehicle crash detection and aids in mitigating or preventing potential risks or threats associated with future traffic events.

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 Road Crash Monitoring utilizes a combination of other services:

1. Postgres - For database,

2. Docker Snapshot - For action recognition related,

3. Visionaire Docker Stream (must be v4.57.17 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:

{
  "dump_interval": 3,      
  "address": "http://192.168.101.105:4006",
}

Parameter	Explanation
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 VM snapshot service is deployed