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  • 1.
    Bhat, Goutam
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Johnander, Joakim
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Danelljan, Martin
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Unveiling the power of deep tracking2018Inngår i: Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part II / [ed] Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu and Yair Weiss, Cham: Springer Publishing Company, 2018, s. 493-509Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking. We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness. We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking. Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy. Extensive experiments are performed on four challenging datasets. On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of >17% in EAO.

  • 2.
    Johnander, Joakim
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Bhat, Goutam
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Danelljan, Martin
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    On the Optimization of Advanced DCF-Trackers2018Inngår i: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I / [ed] Laura Leal-TaixéStefan Roth, Cham: Springer Publishing Company, 2018, s. 54-69Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detector and target state inference based on this detector. Whereas the original formulation includes a closed-form solution for the filter learning, recently introduced improvements to the framework no longer have known closed-form solutions. Instead a large-scale linear least squares problem must be solved each time the detector is updated. We analyze the procedure used to optimize the detector and let the popular scheme introduced with ECO serve as a baseline. The ECO implementation is revisited in detail and several mechanisms are provided with alternatives. With comprehensive experiments we show which configurations are superior in terms of tracking capabilities and optimization performance.

  • 3.
    Johnander, Joakim
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Zenuity, Sweden.
    Danelljan, Martin
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. ETH Zurich, Switzerland.
    Brissman, Emil
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. Saab, Sweden.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten. IIAI, UAE.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    A generative appearance model for end-to-end video object segmentation2019Inngår i: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2019, s. 8945-8954Konferansepaper (Fagfellevurdert)
    Abstract [en]

    One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance. The best performing approaches resort to extensive fine-tuning of a convolutional neural network for this purpose. Besides being prohibitively expensive, this strategy cannot be truly trained end-to-end since the online fine-tuning procedure is not integrated into the offline training of the network. To address these issues, we propose a network architecture that learns a powerful representation of the target and background appearance in a single forward pass. The introduced appearance module learns a probabilistic generative model of target and background feature distributions. Given a new image, it predicts the posterior class probabilities, providing a highly discriminative cue, which is processed in later network modules. Both the learning and prediction stages of our appearance module are fully differentiable, enabling true end-to-end training of the entire segmentation pipeline. Comprehensive experiments demonstrate the effectiveness of the proposed approach on three video object segmentation benchmarks. We close the gap to approaches based on online fine-tuning on DAVIS17, while operating at 15 FPS on a single GPU. Furthermore, our method outperforms all published approaches on the large-scale YouTube-VOS dataset.

  • 4.
    Johnander, Joakim
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Danelljan, Martin
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Khan, Fahad Shahbaz
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking2017Inngår i: 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, s. 55-67Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.

  • 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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Lukezič, Alan
    University of Ljubljana, Slovenia.
    Eldesokey, Abdelrahman
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    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 Results2018Inngår i: 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, 2018, s. 3-53Konferansepaper (Fagfellevurdert)
    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).

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