Academic & Scientific Articles
Permanent URI for this communityhttp://dl.cerist.dz/handle/CERIST/3
Browse
3 results
Search Results
Item A framework for object classification in farfield videos(CERIST, 2014-10-26) Setitra, Insaf; Larabi, SlimaneObject classification in videos is an important step in many applications such as abnormal event detection in video surveillance, traffic 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.Item A framework for Object Classification in Fareld Videos(Springer, 2014-12) Setitra, Insaf; Larabi, SlimaneObject 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.Item Background subtraction algorithms with post processing A review(CERIST, 2014-04-28) Setitra, Insaf; Larabi, SlimaneDue to its several algorithms with their fast implementations, background subtraction becomes a very important step in many computer vision and video surveillance systems which assume static cameras. Literature counts a large number of robust background subtraction algorithms which try each to outperform the others in a quantitative and qualitative manner. This competition can sometimes confuse the user of this kind of process and make the choice of one of them difficult. To overcome this issue we review, in what follows, the background subtraction process by defining it and exploring most used algorithms of background subtraction. We then expose some post processing techniques used to remove superfluous content derived from background subtraction.