ROAM : Recurrently Optimizing Tracking Model
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings 2020 IEEE/CVF International Conference on Computer Vision and Pattern Recognition |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 6717-6726 |
ISBN (electronic) | 9781728171685 |
ISBN (print) | 9781728171692 |
Publication status | Published - Jun 2020 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Publisher | IEEE Computer Society |
ISSN (Print) | 1063-6919 |
ISSN (electronic) | 2575-7075 |
Conference
Title | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) |
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Location | Virtual |
Place | United States |
City | Seattle |
Period | 13 - 19 June 2020 |
Link(s)
Abstract
In this paper, we design a tracking model consisting of response generation and bounding box regression, where the first component produces a heat map to indicate the presence of the object at different positions and the second part regresses the relative bounding box shifts to anchors mounted on sliding-window locations. Thanks to the resizable convolutional filters used in both components to adapt to the shape changes of objects, our tracking model does not need to enumerate different sized anchors, thus saving model parameters. To effectively adapt the model to appearance variations, we propose to offline train a recurrent neural optimizer to update tracking model in a meta-learning setting, which can converge the model in a few gradient steps. This improves the convergence speed of updating the tracking model while achieving better performance. We extensively evaluate our trackers, ROAM and ROAM++, on the OTB, VOT, LaSOT, GOT-10K and TrackingNet benchmark and our methods perform favorably against state-of-the-art algorithms.
Bibliographic Note
Research Unit(s) information for this publication is provided by the author(s) concerned.
Citation Format(s)
ROAM: Recurrently Optimizing Tracking Model. / Yang, Tianyu; Xu, Pengfei; Hu, Runbo et al.
Proceedings 2020 IEEE/CVF International Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers, Inc., 2020. p. 6717-6726 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
Proceedings 2020 IEEE/CVF International Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers, Inc., 2020. p. 6717-6726 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review