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Training Note Video Analytics Left Objects Print
July 2013

This article discusses 3 levels of intelligence for detecting Left Objects which could be a threat to public safety or an aid to identifying the original owner by jumping back to the video recording when the event was detected.
  
IQ-100 

  • This is an entry level application and applies to empty space only and certainly not for any busy environment.  It can detect an object left in an empty scene up to a period which is shorter than the predefined Motion Retention time (MRT).  In case the object is blocked from the camera, the application will reset the timer and start counting again. In case we set the period to be longer than MRT, all left objects will be ignored. MRT pre-definition is required to eliminate various transient or illumination changes.
  • For false alarm reduction, it requires the size of potential objects to be predefined. This helps to eliminate falling leaves on the ground, or a big dog lying down.  
  • It can detect one object at a time. Other objects that are left at the same time would be ignored.

    iq-100_left_object.jpg












IQ-140 & IQ-180

  • IQ-140 can detect objects as small as 8*8 pixels and IQ-180 can detect very small objects of 4*4 pixels in busy scenes. The application needs to be told to expect the level of business of the scene and to relearn the scene if it has changed beyond a certain percentage. Default parameter is 50% for business of scene and an additional 10% change (that is 60% business) for triggering relearning of the scene.   These parameters are different for each site and tuning is essential to improve accuracy.  The point is that the application will take time to relearn the scene.  If the business level hits 70% in the meantime, the application will miss certain changes (including left objects). A message will be displayed on the screen in case someone is monitoring in real time.    

           Image

  • Both applications are capable of detecting multiple left objects.
  • A left object that has a similar colour to the background is the worst case scenario for detection (such as a black bag in a shadowed background).  The applications are able to detect an object that is the same colour as the background with 50% difference in luminance. That ratio comes down when there is significant difference in colour.  A white object against a black background will require 0% difference in luminance.
  • The false alarm rate depends heavily on the amount of moving traffic and changing environment. If we deploy this in an indoor environment with minimal traffic (with 2 to 3 persons through traffic per minute) we would be looking at close to 0 false alarms. If we deploy this in an outdoor environment with foot traffic density similar to a busy city street, we are probably looking at around 1 false alarm per camera per 3 to 4 hours.

 

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