Papers › Staple: Complementary Learners for Real-Time Tracking
Staple: Complementary Learners for Real-Time Tracking
Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Object Tracking | NT-VOT211 | Staple | AUC | 31.29 | #31 of 43 | Archive leaderboard | report |
| Video Object Tracking | NT-VOT211 | Staple | Precision | 39.12 | #31 of 43 | Archive leaderboard | report |
| Visual Object Tracking | TrackingNet | STAPLE_CA | Accuracy | 53.59 | #37 of 40 | Archive leaderboard | report |
| Visual Object Tracking | TrackingNet | STAPLE_CA | Normalized Precision | 60.84 | #37 of 40 | Archive leaderboard | report |
| Visual Object Tracking | TrackingNet | STAPLE_CA | Precision | 46.72 | #37 of 40 | 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.
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