The design of a robust basis for identifying the objects of interest
on the video scenes and evaluating their trajectories along the
time is essential. The aim is to get a high hit rate in these previous
tasks for making easier the subsequent detection of suspicious
behaviors and alarms in the automated video-surveillance application
for shopping malls presented in this paper. Therefore, the
errors in the video pre-processing and human tracking stages
dramatically affect the effectiveness of the final system. Due to
this, these errors must be reduced as much as possible.