Object classification in videos is an important step in many
applications such as abnormal event detection in video surveillance, traf-
fic analysis is urban scenes and behavior control in crowded locations.
In this work, propose a framework for moving object classification in
farfield videos. Much works have been dedicated to accomplish this task.
We overview existing works and combine several techniques to implement
a real time object classifier with offline training phase. We follow three
main steps to classify objects in steady background videos : background
subtraction, object tracking and classification. We measure accuracy of
our classifier by experiments done using the PETS 2009 dataset.
