Struck: Structured Output Tracking with Kernels

By Sam Hare, Amir Saffari, Philip H. S. Torr. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016

1,012 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/61394599ed0aabe04b724c7ca3a778825c7e776f

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In Computer Vision and Pattern Recognition

Citations in the corpus

LaSOT: A High-quality Large-scale Single Object Tracking Benchmark (2020, background): “, CT [91], IVT [70], MIL [3] and Struck [35]).”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “3, we see that: (1) in the success plot, our proposed tracker TGPR outperforms the second best tracker SCM by 8.0%; and (2) in the precision plot, TGPR outperforms the second best tracker Struck by 15.7%.”
DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking (2014, methodology): “In many early methods, features are manually defined and combined [1, 5, 9, 15].”
The Sixth Visual Object Tracking VOT2018 Challenge Results (2018, methodology): “The trackers which have been considered as baselines or state-of-the-art even few years ago, i.e., MIL (A.52), IVT (A.36), Struck [28] and KCF (A.38) are positioned at the lower part of the AR-plots and at the tail of the EAO rank list.”
Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking (2016, methodology): “, Struck [18] and STC [37]) are crucial for effective grayscale-thermal tracking.”
Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models (2017, methodology): “…have been applied, including boosting (Grabner, Grabner, and Bischof 2006; Grabner, Leistner, and Bischof 2008), multiple-instance learning (Babenko, Yang, and Belongie 2011), structured output support vector machine (Hare, Saffari, and Torr 2011), and Gaussian process regression (Gao et al. 2014).”
Online Model Distillation for Efficient Video Inference (2018, methodology): “Tracking: Traditional object tracking methods [20, 12, 15] and more recent methods built upon deep feature hierarchies [27, 45, 17, 31] can be viewed as a form of rapid online learning of appearance models from video.”
Convolutional Features for Correlation Filter Based Visual Tracking (2015, background): “[20], CFLB [16], MIL [3], CT [41], TLD [23], DFT [34], EDFT [15], ASLA [22].”
Is First Person Vision Challenging for Object Tracking? (2020, positive): “[64] proposed EgoTracker, a combination of the Struck [35] and MEEM [89] trackers with a person re-identification module.”