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Johnander, Joakim
Publications (5 of 5) Show all publications
Johnander, J., Danelljan, M., Brissman, E., Khan, F. S. & Felsberg, M. (2019). A generative appearance model for end-to-end video object segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): . Paper presented at IEEE Conference on Computer Vision and Pattern Recognition. 2019, Long Beach, CA, USA, USA, 15-20 June 2019 (pp. 8945-8954). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A generative appearance model for end-to-end video object segmentation
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2019 (English)In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 8945-8954Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, E-ISSN 2575-7075
Keywords
Segmentation; Grouping and Shape; Motion and Tracking
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-161037 (URN)10.1109/CVPR.2019.00916 (DOI)9781728132938 (ISBN)9781728132945 (ISBN)
Conference
IEEE Conference on Computer Vision and Pattern Recognition. 2019, Long Beach, CA, USA, USA, 15-20 June 2019
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research Swedish Research Council
Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2020-01-22Bibliographically approved
Johnander, J., Bhat, G., Danelljan, M., Khan, F. S. & Felsberg, M. (2018). On the Optimization of Advanced DCF-Trackers. In: Laura Leal-TaixéStefan Roth (Ed.), Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I. Paper presented at Conference on Computer Vision (ECCV) Workshops, Munich, Germany, 8-14 September, 2018 (pp. 54-69). Cham: Springer Publishing Company
Open this publication in new window or tab >>On the Optimization of Advanced DCF-Trackers
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2018 (English)In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I / [ed] Laura Leal-TaixéStefan Roth, Cham: Springer Publishing Company, 2018, p. 54-69Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Cham: Springer Publishing Company, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11129
National Category
Engineering and Technology Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-161036 (URN)10.1007/978-3-030-11009-3_2 (DOI)9783030110086 (ISBN)9783030110093 (ISBN)
Conference
Conference on Computer Vision (ECCV) Workshops, Munich, Germany, 8-14 September, 2018
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-30Bibliographically approved
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. C., . . . He, Z. (2018). The Sixth Visual Object Tracking VOT2018 Challenge Results. In: Laura Leal-Taixé and Stefan Roth (Ed.), Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8–14, 2018 Proceedings, Part I. Paper presented at Computer Vision – ECCV 2018 Workshops, Munich, Germany, September 8–14, 2018 (pp. 3-53). Cham: Springer Publishing Company
Open this publication in new window or tab >>The Sixth Visual Object Tracking VOT2018 Challenge Results
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2018 (English)In: 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, p. 3-53Conference paper, Published 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).

Place, publisher, year, edition, pages
Cham: Springer Publishing Company, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11129
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-161343 (URN)10.1007/978-3-030-11009-3_1 (DOI)9783030110086 (ISBN)9783030110093 (ISBN)
Conference
Computer Vision – ECCV 2018 Workshops, Munich, Germany, September 8–14, 2018
Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2020-01-22Bibliographically approved
Bhat, G., Johnander, J., Danelljan, M., Khan, F. S. & Felsberg, M. (2018). Unveiling the power of deep tracking. In: Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu and Yair Weiss (Ed.), Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part II. Paper presented at 15th European Conference on Computer Vision (ECCV). Munich, Germany, 8-14 September, 2018 (pp. 493-509). Cham: Springer Publishing Company
Open this publication in new window or tab >>Unveiling the power of deep tracking
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2018 (English)In: 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, p. 493-509Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Cham: Springer Publishing Company, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11206
National Category
Computer Vision and Robotics (Autonomous Systems) Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-161032 (URN)10.1007/978-3-030-01216-8_30 (DOI)9783030012151 (ISBN)9783030012168 (ISBN)
Conference
15th European Conference on Computer Vision (ECCV). Munich, Germany, 8-14 September, 2018
Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-30Bibliographically approved
Johnander, J., Danelljan, M., Khan, F. S. & Felsberg, M. (2017). DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking. In: Michael Felsberg, Anders Heyden and Norbert Krüger (Ed.), Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I. Paper presented at 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I (pp. 55-67). Springer, 10424
Open this publication in new window or tab >>DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking
2017 (English)In: 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. 55-67Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Engineering
Identifiers
urn:nbn:se:liu:diva-145373 (URN)10.1007/978-3-319-64689-3_5 (DOI)000432085900005 ()9783319646886 (ISBN)9783319646893 (ISBN)
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Note

Funding agencies: SSF (SymbiCloud); VR (EMC2) [2016-05543]; SNIC; WASP; Nvidia

Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-10-16Bibliographically approved
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