Testing IP Thermal Camera (Axis Q1910)

Author: Benros Emata, Published on Jun 01, 2010

In the last 6 months, interest in IP based thermal cameras have increased with Axis, FLIR and Pelco announcing relatively low cost product offerings.

In this test report, we investigate how well the Axis Q1910 thermal camera works in the field, integrating it with Exacq's VMS and with Agent Vi's video analytics. We also contrast the performance of the thermal to Axis's Q1755 HD camera to better understand tradeoffs.

We explore the following questions:

  • What is the setup/configuration required to use the camera?
  • How far can the camera see?
  • What is the maximum FoV width to detect humans?
  • How does the camera's performance compare to a megapixel camera?
  • What palette or color profiles should be used to optimize viewing?
  • How does the camera impact the use of video analytics?
  • What limitations should be factored in for using the camera?

Below is a 1 minute preview of the premium report inside:

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[***************]

Key ******** / ***************

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  • ********: *** ******* ******* ** ***** *** *** ** ***** black *** ** ** *** **** ****** *** * **** monitor ********* ******** ** *** *****.
  • Details ** ********: Fine details of subjects were essentially impossible to detect unless a person was extremely close to the camera. The camera allows you to detect the presence of a subject but little more.
  • Night-time ***********: Not surprisingly, at .3 lux when the Axis Q1755's image was dark, the Q1910 could easily show the outline of a person in the FoV.
  • **********: ** **** **** ** **** ******** ** ** *** feet **** **** *** ****** (************* *** **** **** ***). At **** ********, ******** *** ****** **** *** *** ****** the **** ** ***** (********** ** *** ******* ** ****** slowly).
  • Temperature ******: When the outdoor temperature is similar to human body temperature, visibility of humans can suffer significantly. We note this in the video analytics screencast where we used the camera during a summer day.
  • Light **********: Changes in lighting (like headlights from cars or glare of the sun) were not picked up by the thermal camera at all. We found this to be useful in reducing false alerts for video analytics.
  • Moving *******: Swaying leaves/trees that had a background of grass had minimal impact on the thermal camera (that is, it was hardly visible because of the same temperature). However, swaying leaves/trees that had a background of concrete/pavement were highly visible and can trigger significant false alerts (just like a color camera).

Product ********

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*******

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Physical ******** & *** *********

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Thermal ******* **********

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Day ****** ***** ***** ** ******* *********

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*** ****** *******:

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Maximum ******** ****

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*** ****** *******:

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Video ******* *******

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Using **** *********

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  • ******* ******* ** ***** *** ******* ***** ****** ****** **** analytics
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  • ********* ************ ******* * **** ** ********** *** ******* *** in ********** ********* *** ****'* ********** ***** *** ***** ***** positive ******* ** *********
  • ********* ** *** ************* **********, ***** ******* *** **** ******* body *********** ** ******, ******* ******** ****** ******** *********

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