Mohamed Omran (PhD Student)

MSc Mohamed Omran

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 609
Phone
+49 681 9325 2009
Fax
+49 681 9325 2099
Email
Get email via email

Personal Information

Research Interests

  • Computer Vision (object detection, semantic labeling, looking at people, datasets, differentiable rendering)
  • Machine Learning (deep learning, structured output prediction)

Education

  • 2014–present, Ph.D. student in Computer Science, Max Planck Institute for Informatics
  • 2014, M.Sc. in Visual Computing, Saarland University
  • 2011, B.Sc. in Media Informatics, Ulm University

Recent Positions

Teaching

Reviewing Activities

  • IEEE Intelligent Vehicles Syposium 2015
  • CVPR '18 (Outstanding Reviewer)
  • ACCV '18
  • IEEE TPAMI
  • IJCV
  • IEEE Robotics and Automation Letters
  • IEEE T-CVST

Personal Pages

    Publications

    Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
    M. Omran, C. Lassner,, G. Pons-Moll, P. Gehler and B. Schiele
    International Conference on 3D Vision, 2018
    (Accepted/in press)
    Export
    BibTeX
    @inproceedings{omran2018nbf, TITLE = {Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation}, AUTHOR = {Omran, Mohamed and Lassner,, Christoph and Pons-Moll, Gerard and Gehler, Peter and Schiele, Bernt}, LANGUAGE = {eng}, PUBLISHER = {IEEE}, YEAR = {2018}, PUBLREMARK = {Accepted}, BOOKTITLE = {International Conference on 3D Vision}, ADDRESS = {Verona, Italy}, }
    Endnote
    %0 Conference Proceedings %A Omran, Mohamed %A Lassner,, Christoph %A Pons-Moll, Gerard %A Gehler, Peter %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation : %G eng %U http://hdl.handle.net/21.11116/0000-0001-E564-C %D 2018 %B International Conference on 3D Vision %Z date of event: 2018-09-05 - 2018-09-08 %C Verona, Italy %B International Conference on 3D Vision %I IEEE
    Towards Reaching Human Performance in Pedestrian Detection
    S. Zhang, R. Benenson, M. Omran, J. Hosang and B. Schiele
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 40, Number 4, 2018
    Abstract
    Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the “perfect single frame detector”. We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech pedestrian dataset). After manually clustering the frequent errors of a top detector, we characterise both localisation and background- versus-foreground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve results even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of training and test annotations.
    Export
    BibTeX
    @article{ZBOHS2017, TITLE = {Towards Reaching Human Performance in Pedestrian Detection}, AUTHOR = {Zhang, Shanshan and Benenson, Rodrigo and Omran, Mohamed and Hosang, Jan and Schiele, Bernt}, LANGUAGE = {eng}, ISSN = {0162-8828}, DOI = {10.1109/TPAMI.2017.2700460}, PUBLISHER = {IEEE Computer Society}, ADDRESS = {Los Alamitos, CA}, YEAR = {2018}, DATE = {2018}, ABSTRACT = {Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the {\textquotedblleft}perfect single frame detector{\textquotedblright}. We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech pedestrian dataset). After manually clustering the frequent errors of a top detector, we characterise both localisation and background- versus-foreground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve results even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of training and test annotations.}, JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, VOLUME = {40}, NUMBER = {4}, PAGES = {973--986}, }
    Endnote
    %0 Journal Article %A Zhang, Shanshan %A Benenson, Rodrigo %A Omran, Mohamed %A Hosang, Jan %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Towards Reaching Human Performance in Pedestrian Detection : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-440B-2 %R 10.1109/TPAMI.2017.2700460 %7 2017-05-02 %D 2018 %X Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the “perfect single frame detector”. We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech pedestrian dataset). After manually clustering the frequent errors of a top detector, we characterise both localisation and background- versus-foreground errors. To address localisation errors we study the impact of training annotation noise on the detector performance, and show that we can improve results even with a small portion of sanitised training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech pedestrian dataset, and provide a new sanitised set of training and test annotations. %J IEEE Transactions on Pattern Analysis and Machine Intelligence %O IEEE Trans. Pattern Anal. Mach. Intell. TPAMI %V 40 %N 4 %& 973 %P 973 - 986 %I IEEE Computer Society %C Los Alamitos, CA %@ false
    Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
    E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele and B. Andres
    30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
    Export
    BibTeX
    @inproceedings{levinkov-2017-cvpr, TITLE = {Joint Graph Decomposition and Node Labeling: {P}roblem, Algorithms, Applications}, AUTHOR = {Levinkov, Evgeny and Uhrig, Jonas and Tang, Siyu and Omran, Mohamed and Insafutdinov, Eldar and Kirillov, Alexander and Rother, Carsten and Brox, Thomas and Schiele, Bernt and Andres, Bjoern}, LANGUAGE = {eng}, ISBN = {978-1-5386-0458-8}, DOI = {10.1109/CVPR.2017.206}, PUBLISHER = {IEEE Computer Society}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)}, PAGES = {1904--1912}, ADDRESS = {Honolulu, HI, USA}, }
    Endnote
    %0 Conference Proceedings %A Levinkov, Evgeny %A Uhrig, Jonas %A Tang, Siyu %A Omran, Mohamed %A Insafutdinov, Eldar %A Kirillov, Alexander %A Rother, Carsten %A Brox, Thomas %A Schiele, Bernt %A Andres, Bjoern %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-05DB-2 %R 10.1109/CVPR.2017.206 %D 2017 %B 30th IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2017-07-22 - 2017-07-25 %C Honolulu, HI, USA %B 30th IEEE Conference on Computer Vision and Pattern Recognition %P 1904 - 1912 %I IEEE Computer Society %@ 978-1-5386-0458-8
    The Cityscapes Dataset for Semantic Urban Scene Understanding
    M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth and B. Schiele
    29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016
    Export
    BibTeX
    @inproceedings{Cordts2016, TITLE = {The Cityscapes Dataset for Semantic Urban Scene Understanding}, AUTHOR = {Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-1-4673-8852-8}, DOI = {10.1109/CVPR.2016.350}, PUBLISHER = {IEEE Computer Society}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)}, PAGES = {3213--3223}, ADDRESS = {Las Vegas, NV, USA}, }
    Endnote
    %0 Conference Proceedings %A Cordts, Marius %A Omran, Mohamed %A Ramos, Sebastian %A Rehfeld, Timo %A Enzweiler, Markus %A Benenson, Rodrigo %A Franke, Uwe %A Roth, Stefan %A Schiele, Bernt %+ External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T The Cityscapes Dataset for Semantic Urban Scene Understanding : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-FCED-A %R 10.1109/CVPR.2016.350 %D 2016 %B 29th IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2016-06-26 - 2016-07-01 %C Las Vegas, NV, USA %B 29th IEEE Conference on Computer Vision and Pattern Recognition %P 3213 - 3223 %I IEEE Computer Society %@ 978-1-4673-8852-8
    Weakly Supervised Object Boundaries
    A. Khoreva, R. Benenson, M. Omran, M. Hein and B. Schiele
    29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016
    Abstract
    State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-the-art methods.
    Export
    BibTeX
    @inproceedings{khoreva_cvpr16, TITLE = {Weakly Supervised Object Boundaries}, AUTHOR = {Khoreva, Anna and Benenson, Rodrigo and Omran, Mohamed and Hein, Matthias and Schiele, Bernt}, ISBN = {978-1-4673-8852-8}, DOI = {10.1109/CVPR.2016.27}, PUBLISHER = {IEEE Computer Society}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, ABSTRACT = {State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-the-art methods.}, BOOKTITLE = {29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)}, PAGES = {183--192}, ADDRESS = {Las Vegas, NV, USA}, }
    Endnote
    %0 Conference Proceedings %A Khoreva, Anna %A Benenson, Rodrigo %A Omran, Mohamed %A Hein, Matthias %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Weakly Supervised Object Boundaries : %U http://hdl.handle.net/11858/00-001M-0000-002B-2645-2 %R 10.1109/CVPR.2016.27 %D 2016 %B 29th IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2016-06-26 - 2016-07-01 %C Las Vegas, NV, USA %X State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-the-art methods. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV %B 29th IEEE Conference on Computer Vision and Pattern Recognition %P 183 - 192 %I IEEE Computer Society %@ 978-1-4673-8852-8
    How Far are We from Solving Pedestrian Detection?
    S. Zhang, R. Benenson, M. Omran, J. Hosang and B. Schiele
    29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016
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    BibTeX
    @inproceedings{Shanshan2016CVPR, TITLE = {How Far are We from Solving Pedestrian Detection?}, AUTHOR = {Zhang, Shanshan and Benenson, Rodrigo and Omran, Mohamed and Hosang, Jan and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-1-4673-8852-8}, DOI = {10.1109/CVPR.2016.141}, PUBLISHER = {IEEE Computer Society}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)}, PAGES = {1259--1267}, ADDRESS = {Las Vegas, NV, USA}, }
    Endnote
    %0 Conference Proceedings %A Zhang, Shanshan %A Benenson, Rodrigo %A Omran, Mohamed %A Hosang, Jan %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T How Far are We from Solving Pedestrian Detection? : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-D905-C %R 10.1109/CVPR.2016.141 %D 2016 %B 29th IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2016-06-26 - 2016-07-01 %C Las Vegas, NV, USA %B 29th IEEE Conference on Computer Vision and Pattern Recognition %P 1259 - 1267 %I IEEE Computer Society %@ 978-1-4673-8852-8
    The Cityscapes Dataset
    M. Cordts, M. Omran, S. Ramos, T. Scharwächter, M. Enzweiler, R. Benenson, U. Franke, S. Roth and B. Schiele
    The Future of Datasets in Vision 2015 (CVPR 2015 Workshop), 2015
    Export
    BibTeX
    @inproceedings{cordts2015cityscapes, TITLE = {The Cityscapes Dataset}, AUTHOR = {Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Scharw{\"a}chter, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, LANGUAGE = {eng}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Future of Datasets in Vision 2015 (CVPR 2015 Workshop)}, ADDRESS = {Boston, MA, USA}, }
    Endnote
    %0 Generic %A Cordts, Marius %A Omran, Mohamed %A Ramos, Sebastian %A Scharwächter, Timo %A Enzweiler, Markus %A Benenson, Rodrigo %A Franke, Uwe %A Roth, Stefan %A Schiele, Bernt %+ External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T The Cityscapes Dataset : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-E733-4 %D 2015 %Z name of event: CVPR Workshop on The Future of Datasets in Vision %Z date of event: 2015-06-11 - 2015-07-11 %Z place of event: Boston, MA, USA %B The Future of Datasets in Vision 2015
    Detecting Surgical Tools by Modelling Local Appearance and Global Shape
    D. Bouget, R. Benenson, M. Omran, L. Riffaud, B. Schiele and P. Jannin
    IEEE Transactions on Medical Imaging, Volume 34, Number 12, 2015
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    BibTeX
    @article{958, TITLE = {Detecting Surgical Tools by Modelling Local Appearance and Global Shape}, AUTHOR = {Bouget, David and Benenson, Rodrigo and Omran, Mohamed and Riffaud, Laurent and Schiele, Bernt and Jannin, Pierre}, LANGUAGE = {eng}, ISSN = {0278-0062}, DOI = {10.1109/TMI.2015.2450831}, PUBLISHER = {IEEE}, ADDRESS = {Piscataway, NJ}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, JOURNAL = {IEEE Transactions on Medical Imaging}, VOLUME = {34}, NUMBER = {12}, PAGES = {2603--2617}, }
    Endnote
    %0 Journal Article %A Bouget, David %A Benenson, Rodrigo %A Omran, Mohamed %A Riffaud, Laurent %A Schiele, Bernt %A Jannin, Pierre %+ External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations %T Detecting Surgical Tools by Modelling Local Appearance and Global Shape : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-E35A-6 %R 10.1109/TMI.2015.2450831 %7 2015 %D 2015 %J IEEE Transactions on Medical Imaging %O IEEE Trans. Med. Imaging %V 34 %N 12 %& 2603 %P 2603 - 2617 %I IEEE %C Piscataway, NJ %@ false
    Ten Years of Pedestrian Detection, What Have We Learned?
    R. Benenson, M. Omran, J. Hosang and B. Schiele
    Computer Vision - ECCV 2014 Workshops (ECCV 2014 Workshop CVRSUAD), 2014
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    BibTeX
    @inproceedings{890, TITLE = {Ten Years of Pedestrian Detection, What Have We Learned?}, AUTHOR = {Benenson, Rodrigo and Omran, Mohamed and Hosang, Jan and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-3-319-16180-8}, DOI = {10.1007/978-3-319-16181-5_47}, PUBLISHER = {Springer}, YEAR = {2014}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Computer Vision -- ECCV 2014 Workshops (ECCV 2014 Workshop CVRSUAD)}, EDITOR = {Agapito, Lourdes and Bronstein, Michael M. and Rother, Carsten}, PAGES = {613--627}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {8926}, ADDRESS = {Z{\"u}rich, Switzerland}, }
    Endnote
    %0 Conference Proceedings %A Benenson, Rodrigo %A Omran, Mohamed %A Hosang, Jan %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Ten Years of Pedestrian Detection, What Have We Learned? : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-4C6A-8 %R 10.1007/978-3-319-16181-5_47 %D 2015 %B 2nd Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving %Z date of event: 2014-09-07 - 2014-09-07 %C Zürich, Switzerland %B Computer Vision - ECCV 2014 Workshops %E Agapito, Lourdes; Bronstein, Michael M.; Rother, Carsten %P 613 - 627 %I Springer %@ 978-3-319-16180-8 %B Lecture Notes in Computer Science %N 8926
    Taking a Deeper Look at Pedestrians
    J. Hosang, M. Omran, R. Benenson and B. Schiele
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 2015
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    BibTeX
    @inproceedings{Hosang15cvpr, TITLE = {Taking a Deeper Look at Pedestrians}, AUTHOR = {Hosang, Jan and Omran, Mohamed and Benenson, Rodrigo and Schiele, Bernt}, LANGUAGE = {eng}, DOI = {10.1109/CVPR.2015.7299034}, PUBLISHER = {IEEE Computer Society}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)}, PAGES = {4073--4082}, ADDRESS = {Boston, MA, USA}, }
    Endnote
    %0 Conference Proceedings %A Hosang, Jan %A Omran, Mohamed %A Benenson, Rodrigo %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Taking a Deeper Look at Pedestrians : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-01BF-0 %R 10.1109/CVPR.2015.7299034 %D 2015 %B IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2015-06-08 - 2015-06-10 %C Boston, MA, USA %B IEEE Conference on Computer Vision and Pattern Recognition %P 4073 - 4082 %I IEEE Computer Society
    Pedestrian Detection Meets Stuff
    M. Omran
    PhD Thesis, Universität des Saarlandes, 2014
    Export
    BibTeX
    @mastersthesis{OmranMaster, TITLE = {Pedestrian Detection Meets Stuff}, AUTHOR = {Omran, Mohamed}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2014}, DATE = {2014}, }
    Endnote
    %0 Thesis %A Omran, Mohamed %Y Benenson, Rodrigo %A referee: Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Pedestrian Detection Meets Stuff : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-5477-D %I Universität des Saarlandes %C Saarbrücken %D 2014 %P VII, 75 p. %V master %9 master