A framework for Object Classification in Fareld Videos
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Date
2014-12
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Publisher
Springer
Abstract
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.
Description
Keywords
background subtraction, feature extraction, video analysis, tracking, object classification