ECO: Efficient Convolution Operators for Tracking
Open accessAbstract
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 TempleColor. 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.
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Fast and Accurate Online Video Object Segmentation via Tracking Parts
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GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking
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Structured Siamese Network for Real-Time Visual Tracking
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Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
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- Authors
- Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M.
- Affiliations
- Linköping University (SE)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6931-6939
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2557641257
- Semantic Scholar
- a87cc499cf101b3697cacc65094b4b6590e0d061
- Cited by
- 2,805
- References
- 41
- Citations by year
- 2026: 86 · 2025: 168 · 2024: 284 · 2023: 347 · 2022: 442 · 2021: 457 · 2020: 410 · 2019: 394 · 2018: 201 · 2017: 16
- FWCI
- 81.63
- Type
- conference paper
- Language
- en
- Open access
- green
- urn.kb.se
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- DiVA (Linkoping University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- discriminative correlation filter, object tracking, computational efficiency, overfitting, real-time tracking
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