Staple: Complementary Learners for Real-Time Tracking

Staple: Complementary Learners for Real-Time Tracking

13 Apr 2016 | Luca Bertinetto Jack Valmadre Stuart Golodetz Ondrej Miksik Philip H.S. Torr
Staple is a real-time object tracking method that combines template-based and color-based models to achieve robust performance. It uses a ridge regression framework to learn a model that is inherently robust to both color changes and deformations. The method outperforms existing trackers in multiple benchmarks, including VOT14 and OTB, and runs at speeds exceeding 80 FPS. Staple combines two image patch representations that are sensitive to complementary factors, enabling it to handle both color variations and fast deformations. The method uses a dense translation search to combine scores from two models, which are similar in magnitude and indicative of their reliability. This approach allows the tracker to maintain real-time performance while achieving high accuracy. The method is evaluated on two popular benchmarks, VOT14 and OTB, and shows significant improvements in accuracy and robustness compared to existing methods. The results demonstrate that Staple is a highly effective and efficient tracking method that can be applied to a wide range of real-time applications.Staple is a real-time object tracking method that combines template-based and color-based models to achieve robust performance. It uses a ridge regression framework to learn a model that is inherently robust to both color changes and deformations. The method outperforms existing trackers in multiple benchmarks, including VOT14 and OTB, and runs at speeds exceeding 80 FPS. Staple combines two image patch representations that are sensitive to complementary factors, enabling it to handle both color variations and fast deformations. The method uses a dense translation search to combine scores from two models, which are similar in magnitude and indicative of their reliability. This approach allows the tracker to maintain real-time performance while achieving high accuracy. The method is evaluated on two popular benchmarks, VOT14 and OTB, and shows significant improvements in accuracy and robustness compared to existing methods. The results demonstrate that Staple is a highly effective and efficient tracking method that can be applied to a wide range of real-time applications.
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[slides and audio] Staple%3A Complementary Learners for Real-Time Tracking