| With augmenting security concerns and decreasing costs of surveillance and computing equipment, research on 
automated systems for object detection has been increasing, but the majority of the studies focus their attention on 
sequences  where  high-resolution  objects  are  of  interest.  The  main  objective  of  the  work  reported  here  is  the 
detection and extraction of information of low-resolution objects (e.g., objects that are so small or so far away 
from the camera that they  occupy  only  tens  of  pixels) in  order to  provide a  base  for higher  level  information 
operations  such  as  classification  and  behavioral  analysis.  The  system  proposed  is  composed  of  four  stages 
(preprocessing,  background  modeling,  information  extraction,  and  post  processing)  and  uses  context-based 
region-of-importance selection, histogram equalization, background subtraction, biological motion analysis, and 
morphological filtering techniques. The result is a system capable of detecting and tracking low -resolution objects in a controlled background scene 
which can be a base for systems with higher complexity.
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