Browsing by Author "Larabi, Slimane"

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    A framework for Object Classification in Fareld Videos
    (Springer, 2014-12) Setitra, Insaf; Larabi, Slimane
    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.
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    A framework for object classification in farfield videos
    (CERIST, 2014-10-26) Setitra, Insaf; Larabi, Slimane
    Object 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.
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    A study on discrimination of SIFT feature applied to binary images
    (CERIST, 2015-10-04) Setitra, Insaf; Larabi, Slimane
    Scale Invariant Feature Transform (SIFT) since its first apparition in 2004 has been (and still is) extensively used in computer vision to classify and match objects in RGB and grey level images and videos. However, since the descriptor used in SIFT approach is based on gradient magnitude and orientation, it has always been considered as texture feature and received less interest when treating binary images. In this work we investigate the power of discrimination of SIFT applied to binary images. A theoretical and experimental studies show that SIFT can still describe shapes and can be used to distinguish objects of several classes.
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    Background subtraction algorithms with post processing A review
    (CERIST, 2014-04-28) Setitra, Insaf; Larabi, Slimane
    Due 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.
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    Head Pose Estimation from Depth Map
    (CERIST, 2015-04-01) Kherchi, Asma Manel; Larabi, Slimane
    In this paper we propose a new method for head pose estimation using the depth sensor Kinect. Our approach infers the head pose based on the symmetry or asymmetry computed on the depth map of the face. This approach does not require the location of the nose or any other feature on the face such has been done in many works, but uses only the depth map of the face. Two features are proposed for characterizing the pan, roll and tilt rotation of the head. The first one, measures the area of nearest region of the face relatively to the face area. The second one, it concerns the symmetry on the depth map of the face. Experiments are conducted on our acquired data. The obtained results are promising and demonstrate the useful of the proposed features.