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ECO: Efficient Convolution Operators for Tracking
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2017 (English)In: 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), IEEE , 2017, p. 6931-6939Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting. In this work, we tackle the key causes behind the problems of computational complexity and over-fitting, with the aim of simultaneously improving both speed and performance. We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples; (iii) a conservative model update strategy with improved robustness and reduced complexity. We perform comprehensive experiments on four benchmarks: VOT2016, UAV123, OTB-2015, and Temple-Color. When using expensive deep features, our tracker provides a 20-fold speedup and achieves a 13.0% relative gain in Expected Average Overlap compared to the top ranked method [12] in the VOT2016 challenge. Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.

Place, publisher, year, edition, pages
IEEE , 2017. p. 6931-6939
Series
IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-144284DOI: 10.1109/CVPR.2017.733ISI: 000418371407004ISBN: 978-1-5386-0457-1 OAI: oai:DiVA.org:liu-144284DiVA: diva2:1173604
Conference
30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Note

Funding Agencies|SSF (SymbiCloud); VR (EMC2) [2016-05543]; SNIC; WASP; Visual Sweden; Nvidia

Available from: 2018-01-12 Created: 2018-01-12 Last updated: 2018-01-12

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