Papers › ECO: Efficient Convolution Operators for Tracking
ECO: Efficient Convolution Operators for Tracking
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
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 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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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Object Tracking | TrackingNet | ECO | Accuracy | 56.13 | #36 of 40 | Archive leaderboard | report |
| Visual Object Tracking | TrackingNet | ECO | Normalized Precision | 62.14 | #36 of 40 | Archive leaderboard | report |
| Visual Object Tracking | TrackingNet | ECO | Precision | 48.86 | #36 of 40 | Archive leaderboard | report |
| Visual Object Tracking | VOT2017/18 | ECO | Expected Average Overlap (EAO) | 0.280 | #13 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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