Locally Orderless Tracking

By Shaul Oron, Aharon Bar-Hillel, Dan Levi, Shai Avidan. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

490 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b817d78457a2a28e62d343f534fca10b0d020ed8

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In Computer Vision and Pattern Recognition

Citations in the corpus

Object Tracking Benchmark (2015, background): “Some trackers perform better when the scale factor is smaller, such as L1APG, MT-T, LOT and CPF.”
Learning Spatially Regularized Correlation Filters for Visual Tracking (2015, methodology): “We provide a comparison of our tracker with 24 state-of-the-art methods from the literature: MIL [2], IVT [28], CT [36], TLD [22], DFT [29], EDFT [12], ASLA [21], L1APG [3], CSK [19], SCM [37], LOT [26], CPF [27], CXT [11], Frag [1], Struck [16], LSHT [17], LSST [32], ACT [10], KCF [20], CFLB [14], ”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “These trackers include Struck [8], SCM [45], TLD [15], ASLA [14], VTD [17], VTS [18], CXT [4], LSK [24], CSK [10], MTT [44] and LOT [27].”
Convolutional Features for Correlation Filter Based Visual Tracking (2015, methodology): “Initially, most tracking approaches relied on using only image intensity information or simple color transformations [4, 30, 32] for feature representation.”
Gesture recognition for human-robot collaboration: A review (2017, background): “Some other researchers also improved PF resampling strategy (Li et al., 2012; Rinc (cid:1) on et al., 2011).”
Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014, methodology): “…are: scale mean-shift (SMS) tracker [7], fragment tracker (Frag) [1], semi-supervised Boosting tracker (SSB) [12], local orderless tracker (LOT) [21], incremental visual tracking (IVT) method [22], online AdaBoost tracker (OAB) [11], multiple instance learning tracker (MIL) [2], visual tracking…”
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].”
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.”
Online Object Tracking: A Benchmark (2013, background): “Some trackers perform better when the scale factor is smaller, such as L1APG, MT-T, LOT and CPF.”
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (2014, methodology): “Precision plots of OPE − deformation (19) SAMF [0.814] KCF [0.740] CN [0.620] SCM [0.586] DFT [0.537] Struck [0.521] TLD [0.512] VTD [0.501] CPF [0.494] LOT [0.487] (d) 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Overlap threshold”
Discriminative Scale Space Tracking (2016, methodology): “The trackers used for our comparison are: MIL [18], IVT [27], CT [17], TLD [28], DFT [6], EDFT [29], ASLA [5], L1APG [14], CSK [1], SCM [30], LOT [31], CPF [32], CXT [33], Frag [34], Struck [3], LSHT [4], LSST [35], KCF [9] and SAMF [23].”
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”
Visual Tracking: an Experimental Survey (2013, unknown): “[LOT] Locally Orderless Tracking: The tracker [42] offers adaptation in object appearance by matching with flexible rigidity.”
Adaptive Color Attributes for Real-Time Visual Tracking (2014, methodology): “CT [27] LSST [24] Frag [1] L1APG [2] LOT [18] ASLA [10] TLD [11] SCM [28] EDFT [6] CSK [9] DFT [20] CXT [5] CPF [19] LSHT [8] Struck [7] CN2 CN Median CLE 78.”
Tracking via Robust Multi-task Multi-view Joint Sparse Representation (2013, unknown): “html[24]; http://lrs.”
Template Matching with Deformable Diversity Similarity (2016, background): “[17] extend this idea by measuring one-to-one distance in xyRGB space.”