Thermal Analytics Configurations - Details

NOTICE

COMPLEX PROCEDURE REQUIRED.
The resolution of this article has many complex steps that may result in unforeseen results if not performed correctly. If you are at all unfamiliar with the requirements, please contact Product Support Services for assistance.

Issue

Thermal Analytics Configuration Checklist Items

Product Line

Pelco Video Management

Environment

Thermal TI and MileStone Software

Cause

Configuration of Thermal Analytics

Resolution

The Purpose of this Article is to explain some of the Intricacies of configuring Thermal Analytics:

Below is the Analytic Configuration Screen, along with Items that need to be configured properly for Accurate Thermal Detection.

 

Fine Tuning: This can be changed according to the need of the customer, but the difference between Aggressive versus any other option is the high sensitivty of the Aggressive Option can cause False Triggers.

Emmissivity: This is the measure of the efficiency in which a surface emits thermal energy.  It is defined as the fraction of energy being emitted relative to that emitted by a thermally black surface (a black body).  A black body is a material that is a perfect emitter of heat energy and has an emissivity value of 1.  

***NOTE: This setting is crucial to the accuracy of the thermal detection.  If an object or surface is highly reflective this setting can address that by adjusting the emissivity rating.  For instance: Black Surface = 1, White Surface = 0.

Custom (Material): If you are unsure of the emissivity rating of an object you can select the Custom drop down menu and Select the type of Material that you are using i.e. Aluminum, Steel, etc.

Zone Settings:In Zone Settings, you can configure the Zone for different types of Detection and which can add to the accuracy of the Thermal Detection.  ***More to come on this subject as I investigate.

 

Johnson's Criteria

Divides Night Vision visual categories based on the following levels:

Detection: An Object is present. (1.5 pixels across target)

Recognition: The Type of target can be established. For example: A Person vs A Car (6 pixels across target)

ID: A Specific Object can be discerned.  For example: A Woman vs A Man or A Specific Car (12 pixels across target)