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  • 1.
    Eldesokey, Abdelrahman
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Confidence Propagation through CNNs for Guided Sparse Depth Regression2019In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0182-8828Article in journal (Refereed)
    Abstract [en]

    Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open research problem with numerous applications in autonomous driving, robotics, and surveillance. In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. We also propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. To integrate structural information, we also investigate fusion strategies to combine depth and RGB information in our normalized convolution network framework. In addition, we introduce the use of output confidence as an auxiliary information to improve the results. The capabilities of our normalized convolution network framework are demonstrated for the problem of scene depth completion. Comprehensive experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The results clearly demonstrate that the proposed approach achieves superior performance while requiring only about 1-5% of the number of parameters compared to the state-of-the-art methods.

  • 2.
    Eldesokey, Abdelrahman
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ellipse Detection for Visual Cyclists Analysis “In the Wild”2017In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, p. 319-331Conference paper (Refereed)
    Abstract [en]

    Autonomous driving safety is becoming a paramount issue due to the emergence of many autonomous vehicle prototypes. The safety measures ensure that autonomous vehicles are safe to operate among pedestrians, cyclists and conventional vehicles. While safety measures for pedestrians have been widely studied in literature, little attention has been paid to safety measures for cyclists. Visual cyclists analysis is a challenging problem due to the complex structure and dynamic nature of the cyclists. The dynamic model used for cyclists analysis heavily relies on the wheels. In this paper, we investigate the problem of ellipse detection for visual cyclists analysis in the wild. Our first contribution is the introduction of a new challenging annotated dataset for bicycle wheels, collected in real-world urban environment. Our second contribution is a method that combines reliable arcs selection and grouping strategies for ellipse detection. The reliable selection and grouping mechanism leads to robust ellipse detections when combined with the standard least square ellipse fitting approach. Our experiments clearly demonstrate that our method provides improved results, both in terms of accuracy and robustness in challenging urban environment settings.

  • 3.
    Eldesokey, Abdelrahman
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Inception Institute of Artificial Intelligence Abu Dhabi, UAE.
    Propagating Confidences through CNNs for Sparse Data Regression2019In: British Machine Vision Conference 2018, BMVC 2018, BMVA Press , 2019Conference paper (Refereed)
    Abstract [en]

    In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.

  • 4.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kristan, Matej
    University of Ljubljana, Slovenia.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Leonardis, Ales
    University of Birmingham, England.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Berg, Amanda
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Syst Tekn AB, Linkoping, Sweden.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Syst Tekn AB, Linkoping, Sweden.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Garcia-Martin, Alvaro
    University of Autonoma Madrid, Spain.
    Solis Montero, Andres
    University of Ottawa, Canada.
    Varfolomieiev, Anton
    Kyiv Polytech Institute, Ukraine.
    Erdem, Aykut
    Hacettepe University, Turkey.
    Han, Bohyung
    POSTECH, South Korea.
    Chang, Chang-Ming
    University of Albany, GA USA.
    Du, Dawei
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Erdem, Erkut
    Hacettepe University, Turkey.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    ARC Centre Excellence Robot Vis, Australia; CSIRO, Australia.
    Zhao, Fei
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Bunyak, Filiz
    University of Missouri, MO 65211 USA.
    Battistone, Francesco
    Parthenope University of Naples, Italy.
    Zhu, Gao
    University of Missouri, Columbia, USA.
    Seetharaman, Guna
    US Navy, DC 20375 USA.
    Li, Hongdong
    ARC Centre Excellence Robot Vis, Australia.
    Qi, Honggang
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Bischof, Horst
    Graz University of Technology, Austria.
    Possegger, Horst
    Graz University of Technology, Austria.
    Nam, Hyeonseob
    NAVER Corp, South Korea.
    Valmadre, Jack
    University of Oxford, England.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Feng, Jiayi
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Lang, Jochen
    University of Ottawa, Canada.
    Martinez, Jose M.
    University of Autonoma Madrid, Spain.
    Palaniappan, Kannappan
    University of Missouri, MO 65211 USA.
    Lebeda, Karel
    University of Surrey, England.
    Gao, Ke
    University of Missouri, MO 65211 USA.
    Mikolajczyk, Krystian
    Imperial Coll London, England.
    Wen, Longyin
    University of Albany, GA USA.
    Bertinetto, Luca
    University of Oxford, England.
    Poostchi, Mahdieh
    University of Missouri, MO 65211 USA.
    Maresca, Mario
    Parthenope University of Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Tang, Ming
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Baek, Mooyeol
    POSTECH, South Korea.
    Fan, Nana
    Harbin Institute Technology, Peoples R China.
    Al-Shakarji, Noor
    University of Missouri, MO 65211 USA.
    Miksik, Ondrej
    University of Oxford, England.
    Akin, Osman
    Hacettepe University, Turkey.
    Torr, Philip H. S.
    University of Oxford, England.
    Huang, Qingming
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Martin-Nieto, Rafael
    University of Autonoma Madrid, Spain.
    Pelapur, Rengarajan
    University of Missouri, MO 65211 USA.
    Bowden, Richard
    University of Surrey, England.
    Laganiere, Robert
    University of Ottawa, Canada.
    Krah, Sebastian B.
    Fraunhofer IOSB, Germany.
    Li, Shengkun
    University of Albany, GA USA.
    Yao, Shizeng
    University of Missouri, MO 65211 USA.
    Hadfield, Simon
    University of Surrey, England.
    Lyu, Siwei
    University of Albany, GA USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Golodetz, Stuart
    University of Oxford, England.
    Hu, Tao
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Mauthner, Thomas
    Graz University of Technology, Austria.
    Santopietro, Vincenzo
    Parthenope University of Naples, Italy.
    Li, Wenbo
    Lehigh University, PA 18015 USA.
    Huebner, Wolfgang
    Fraunhofer IOSB, Germany.
    Li, Xin
    Harbin Institute Technology, Peoples R China.
    Li, Yang
    Zhejiang University, Peoples R China.
    Xu, Zhan
    Zhejiang University, Peoples R China.
    He, Zhenyu
    Harbin Institute Technology, Peoples R China.
    The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results2016In: Computer Vision – ECCV 2016 Workshops. ECCV 2016. / [ed] Hua G., Jégou H., SPRINGER INT PUBLISHING AG , 2016, p. 824-849Conference paper (Refereed)
    Abstract [en]

    The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

  • 5.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Leonardis, Aleš
    University of Birmingham, United Kingdom.
    Matas, Jirí
    Czech Technical University, Czech Republic.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pflugfelder, Roman
    Austrian Institute of Technology, Austria / TU Wien, Austria.
    Zajc, Luka Cehovin
    University of Ljubljana, Slovenia.
    Vojírì, Tomáš
    Czech Technical University, Czech Republic.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Lukezič, Alan
    University of Ljubljana, Slovenia.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Fernández, Gustavo
    García-Martín, Álvaro
    Iglesias-Arias, Álvaro
    Alatan, A. Aydin
    González-García, Abel
    Petrosino, Alfredo
    Memarmoghadam, Alireza
    Vedaldi, Andrea
    Muhič, Andrej
    He, Anfeng
    Smeulders, Arnold
    Perera, Asanka G.
    Li, Bo
    Chen, Boyu
    Kim, Changick
    Xu, Changsheng
    Xiong, Changzhen
    Tian, Cheng
    Luo, Chong
    Sun, Chong
    Hao, Cong
    Kim, Daijin
    Mishra, Deepak
    Chen, Deming
    Wang, Dong
    Wee, Dongyoon
    Gavves, Efstratios
    Gundogdu, Erhan
    Velasco-Salido, Erik
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yang, Fan
    Zhao, Fei
    Li, Feng
    Battistone, Francesco
    De Ath, George
    Subrahmanyam, Gorthi R. K. S.
    Bastos, Guilherme
    Ling, Haibin
    Galoogahi, Hamed Kiani
    Lee, Hankyeol
    Li, Haojie
    Zhao, Haojie
    Fan, Heng
    Zhang, Honggang
    Possegger, Horst
    Li, Houqiang
    Lu, Huchuan
    Zhi, Hui
    Li, Huiyun
    Lee, Hyemin
    Chang, Hyung Jin
    Drummond, Isabela
    Valmadre, Jack
    Martin, Jaime Spencer
    Chahl, Javaan
    Choi, Jin Young
    Li, Jing
    Wang, Jinqiao
    Qi, Jinqing
    Sung, Jinyoung
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Henriques, Joao
    Choi, Jongwon
    van de Weijer, Joost
    Herranz, Jorge Rodríguez
    Martínez, José M.
    Kittler, Josef
    Zhuang, Junfei
    Gao, Junyu
    Grm, Klemen
    Zhang, Lichao
    Wang, Lijun
    Yang, Lingxiao
    Rout, Litu
    Si, Liu
    Bertinetto, Luca
    Chu, Lutao
    Che, Manqiang
    Maresca, Mario Edoardo
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yang, Ming-Hsuan
    Abdelpakey, Mohamed
    Shehata, Mohamed
    Kang, Myunggu
    Lee, Namhoon
    Wang, Ning
    Miksik, Ondrej
    Moallem, P.
    Vicente-Moñivar, Pablo
    Senna, Pedro
    Li, Peixia
    Torr, Philip
    Raju, Priya Mariam
    Ruihe, Qian
    Wang, Qiang
    Zhou, Qin
    Guo, Qing
    Martín-Nieto, Rafael
    Gorthi, Rama Krishna
    Tao, Ran
    Bowden, Richard
    Everson, Richard
    Wang, Runling
    Yun, Sangdoo
    Choi, Seokeon
    Vivas, Sergio
    Bai, Shuai
    Huang, Shuangping
    Wu, Sihang
    Hadfield, Simon
    Wang, Siwen
    Golodetz, Stuart
    Ming, Tang
    Xu, Tianyang
    Zhang, Tianzhu
    Fischer, Tobias
    Santopietro, Vincenzo
    Štruc, Vitomir
    Wei, Wang
    Zuo, Wangmeng
    Feng, Wei
    Wu, Wei
    Zou, Wei
    Hu, Weiming
    Zhou, Wengang
    Zeng, Wenjun
    Zhang, Xiaofan
    Wu, Xiaohe
    Wu, Xiao-Jun
    Tian, Xinmei
    Li, Yan
    Lu, Yan
    Law, Yee Wei
    Wu, Yi
    Demiris, Yiannis
    Yang, Yicai
    Jiao, Yifan
    Li, Yuhong
    Zhang, Yunhua
    Sun, Yuxuan
    Zhang, Zheng
    Zhu, Zheng
    Feng, Zhen-Hua
    Wang, Zhihui
    He, Zhiqun
    The Sixth Visual Object Tracking VOT2018 Challenge Results2019In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8–14, 2018 Proceedings, Part I / [ed] Laura Leal-Taixé and Stefan Roth, Cham: Springer Publishing Company, 2019, p. 3-53Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

  • 6. Nyberg, Adam
    et al.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bergström, David
    Gustafsson, David
    Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training2018Conference paper (Refereed)
1 - 6 of 6
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