Transfer Learning Based Visual Tracking with Gaussian Processes Regression

By Jin Gao, Haibin Ling, Weiming Hu, Junliang Xing. 2014

454 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Global and Planetary Change

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Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, background): “, TUM Textureless, Object Segmentation, Object Discovery and PTB, can be found in [7, 61, 73] and [29], respectively.”
Learning Multi-domain Convolutional Neural Networks for Visual Tracking (2015, methodology): “…evaluation (OPE) is employed to compare our algorithm with the six state-of-the-art trackers including MUSTer [21], CNN-SVM [20], MEEM [42], TGPR [12], DSST [6] and KCF [18], as well as the top 2 trackers included in the benchmark—SCM [44] Figure 4: The success plots for eight challenge…”
Transferring Rich Feature Hierarchies for Robust Visual Tracking (2015, methodology): “For a more complete comparison, we also include a recent tracker TGPR [7].”
Understanding and Diagnosing Visual Tracking Systems (2015, methodology): “Thanks to advances made by machine learning researchers, many sophisticated techniques have been applied to visual tracking, including boosting [12, 13], multiple-instance learning [3], structured output SVM [14], Gaussian process regression [11], and deep learning [36, 34, 16].”
ECO: Efficient Convolution Operators for Tracking (2016, methodology): “OTB2015 Dataset: We compare our tracker with 20 stateof-the-art methods: TLD [22], Struck [19], CFLB [16], ACT [13], TGPR [17], KCF [20], DSST [7], SAMF [25], MEEM [38], DAT [33], LCT [28], HCF [27], SRDCF [9], SRDCFad [10], DeepSRDCF [8], Staple [1], MDNet [31], SiameseFC [2], TCNN [30] and C-COT [”
UvA-DARE (Digital Academic Repository) Siamese Instance Search for Tracking (2016, methodology): “The included recent trackers are TGPR [9], MEEM [56], SO-DLT [47], KCFDP [19] and MUSTer [18].”
The Visual Object Tracking VOT2015 Challenge Results (2015, methodology): “Submitted by VOT Committee The TGPR tracker [19] models the probability of target appearance using Gaussian Process Regression.”
Staple: Complementary Learners for Real-Time Tracking (2015, methodology): “Moreover, our method also outperforms recent trackers published after the benchmark such as MEEM [41], DSST [8], TGPR [13], EBT [38] and also trackers that make use of deep conv-nets like CNN-SVM [19] and SO-DLT [37], while running at a signiﬁ-cantly higher frame rate.”
Hierarchical Convolutional Features for Visual Tracking (2015, background): “These trackers can be broadly categorized into three classes: (i) deep learning tracker DLT [30] (ii) correlation filter trackers including the CSK [16], STC [35], and KCF [17]; and (iii) representative tracking algorithms using single or multiple online classifiers, including the MIL [1], Struck [1”
VITAL: VIsual Tracking via Adversarial Learning (2018, methodology): “We compare VITAL with 29 trackers from the OTB-2013 benchmark [59] and other 28 stateof-the-art trackers including DSST [10], KCF [22], TGPR [16], MEEM [63], RPT [34], LCT [37], MUSTer [25], HCFT [36], FCNT [57], SRDCF [12], CNN-SVM [24], DeepSRDCF [11], DAT [41], Staple [3], SRDCFdecon [13], CCOT [”
Convolutional Features for Correlation Filter Based Visual Tracking (2015, methodology): “We additionally include the top 6 existing trackers in our OTB evaluation, namely SRDCF, SAMF, MEEM, DSST, KCF and TGPR. Figure 9 contains the survival curves of all trackers.”
Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models (2017, methodology): “Many advanced machine learning techniques 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 Gauss”
Large Margin Object Tracking with Circulant Feature Maps (2017, methodology): “We evaluate LMCF with 9 state-of-the-art trackers designed with conventional hand-crafted features including Struck [13], MEEM [31], TGPR [10], DLSSVM [22], Sta-ple [2], KCF [14], RPT [20], DSST [5] and SAMF [19].”
Long-term correlation tracking (2015, methodology): “Struck [9], CT [29], and ASLA [13]); and (iii) tracking by multiple online classifiers (TLD [14], SCM [30], MEEM [27], and TGPR [8]).”
Object Tracking Benchmark (2015, methodology): “probability of target appearance, and two types of labeled samples were used for improving the tracking performance [25], and some promising results have been demonstrated TABLE 4 SRER Evaluation Results on the TB-50 Dataset”