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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Master thesis : Content-aware retargeting of broadcast videos

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Pagliarello, Lorenzo ULiège
Promotor(s) : Geurts, Pierre ULiège ; Barnich, Olivier
Date of defense : 27-Jun-2022/28-Jun-2022 • Permalink : http://hdl.handle.net/2268.2/14332
Details
Title : Master thesis : Content-aware retargeting of broadcast videos
Translated title : [fr] Reciblage intelligent du format de vidéos télédiffusées
Author : Pagliarello, Lorenzo ULiège
Date of defense  : 27-Jun-2022/28-Jun-2022
Advisor(s) : Geurts, Pierre ULiège
Barnich, Olivier 
Committee's member(s) : Van Droogenbroeck, Marc ULiège
Wehenkel, Louis ULiège
Language : English
Number of pages : 96
Keywords : [en] Video retargeting
[en] Video saliency detection
[en] One dimensional cropping
[en] Dynamic programming
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] Video retargeting, or the challenge of transforming a video from one aspect ratio to another, has become a source of great interest in recent years. While the de-facto standard for filming productions has been 16:9 for a long time, the growth of social media and the broadening of screen sizes demand for an automatic conversion procedure. With this thesis, we provide an overview of the current practices for this field both in the literature and in the industry. We discuss why one dimensional cropping should be preferred over other hybrid techniques in the context of the broadcast industry.

Resulting from this study, we introduce our own modular framework composed of two subsequent computational blocs. On one hand, the first module comprises a state-of-the-art video saliency detection model which locates and quantifies relevant information. As part of our contributions, we build our own saliency dataset called EVS-Sal and fine-tune the deep network to specialize its detections for soccer content. On the other hand, the second module is responsible for the selection of cropped salient information while ensuring temporal consistency. For this purpose, we explore both global and local optimizations respectively with the dynamic programming paradigm and with a “select and filter” approach.

Finally, we show that our methods outperform current one dimensional retargeting algorithms on a variety of general videos. Additionally, we extend this analysis with the creation of our own soccer retargeting dataset called EVS-Ret. With the latter, we demonstrate that our framework brings results near inter-human agreement and that the semantics of soccer are correctly captured by the re-trained saliency model.


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Author

  • Pagliarello, Lorenzo ULiège Université de Liège > Master ingé. civ. info., à fin.

Promotor(s)

Committee's member(s)

  • Van Droogenbroeck, Marc ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
    ORBi View his publications on ORBi
  • Wehenkel, Louis ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes stochastiques
    ORBi View his publications on ORBi
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