Distribution fields for tracking

By Laura Sevilla-Lara, Eric Learned-Miller. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

583 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2e42be2f7da0432450416311f49e24643fbf4567

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In Computer Vision and Pattern Recognition

Citations in the corpus

Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014, methodology): “Numerous algorithms have been proposed with focus on effective appearance models, which are based on the target appearance [8,1,28,22,17,18,19,23,21,31] or the difference between appearances of the target and its local background [11,16,14,2,30,15].”
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].”
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network (2015, unknown): “We compared our method with top 10 trackers in (Wu et al., 2013), which include SCM (Zhong et al., 2012), Struck (Hare et al., 2011), TLD (Kalal et al., 2012), ASLA (Jia et al., 2012), CXT (Dinh et al., 2011), VTD (Kwon & Lee, 2010), VTS (Kwon & Lee, 2011), CSK (Henriques et al., 2012), LSK (Liu et ”
In defense of color-based model-free tracking (2015, methodology): “ACT [14] CT [47] DFT [39] FoT [43] HT [17] IVT [38] KCF [23] LGT [42] MIL [4] PLT [26] SPOT [48] Struck [21] TLD [25] (b) Ranking results VOT13.”
The Visual Object Tracking VOT2014 Challenge Results (2014, unknown): “Field Tracker (qwsEDFT) K. ¨Ofj¨all, M. Felsberg ( { kristoﬀer.ofjall, michael.felsberg } @liu.se ) A model matching approach where the tracked model is represented by a channel distribution ﬁeld.”
Adaptive Color Attributes for Real-Time Visual Tracking (2014, methodology): “Most state-of-the-art trackers either rely on intensity or texture information [7, 27, 11, 5, 20].”
Accurate Scale Estimation for Robust Visual Tracking (2014, methodology): “Ours [0.745] Struck [0.659] ASLA [0.612] SCM [0.610] TLD [0.509] LSHT [0.508] EDFT [0.505] CSK [0.502] L1APG [0.472] LOT [0.467] DFT [0.441] CT [ 0.344] 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 Overlap threshold Overlap Precision”
Robust Visual Tracking via Convolutional Networks Without Training (2015, background): “These results show that all the filers, soft shrinkage, and model update components play key roles in the proposed algorithm for robust visual tracking.”
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking (2016, methodology): “This is efficiently implemented as a scaled inverse DFT of the non-zero Fourier coefficients ŝ[k], k = −K, . . . ,K.”
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (2014, methodology): “Success plots of OPE − deformation (19) SAMF [0.624] KCF [0.534] SCM [0.448] DFT [0.439] CN [0.438] Struck [0.393] TLD [0.378] VTD [0.377] LSK [0.377] ASLA [0.372] (c) 0 10 20 30 40 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Location error threshold”
Object Tracking Benchmark (2015, methodology): “When the tracking problem is posed within an optimization framework, assuming the objective function is differentiable with respect to the motion parameters, gradient descent methods can be used to locate the target efﬁcient-ly [37, 16, 20, 49].”
Online Object Tracking: A Benchmark (2013, background): “However, these objective functions are usually nonlinear and contain many local minima.”
Discriminative Scale Space Tracking (2016, methodology): “This is achieved using either generative [6], [14], Fig.”
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking (2016, methodology): “We perform a comprehensive comparison with 17 recent state-of-the-art trackers: EDFT [7], LSHT [12], DFT [24], ASLA [15], TLD [16], Struck [11], CFLB [8], ACT [6], TGPR [9], KCF [13], DSST [2], SAMF [19], DAT [23], MEEM [29], LCT [22], HCF [21] and SRDCF [5].”
Learning Spatially Regularized Correlation Filters for Visual Tracking (2015, methodology): “ˆ z l · ˆ f l be the DFT of the detection scores S f ( z ) evaluated at the sample z .”
Real-time part-based visual tracking via adaptive correlation filters (2015, methodology): “We compared the proposed algorithm with thirteen state-of-the-art visual trackers: Frag[1], TLD [21], IVT [34], DFT [35], ORIA [44], LOT [32], CXT [13], L1apg [5], MTT [53], ASLA [20], MIL [4], Struck [16] and KCF [17].”
High-Speed Tracking with Kernelized Correlation Filters (2014, unknown): “In this paper, we develop tools to analytically incorporate thousands of samples • The authors are with the Institute of Systems and Robotics, University of Coimbra.”
Visual Tracking via Locality Sensitive Histograms (2013, methodology): “For presentation clarity, we only show the results by the trackers with the top average success rate, i.e., CT [27], MIL [2], TLD [11], Struck [8], DFT [19] and MTT [28] .”