Visual Tracking via Locality Sensitive Histograms

By Shengfeng He, Qingxiong Yang, Rynson W. H. Lau, Jiang Wang, Ming-Hsuan Yang. 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

328 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/da692f33c29c9dc966f8c3d69b34fcdf6ed4a36f

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In Computer Vision and Pattern Recognition

Citations in the corpus

Accurate Scale Estimation for Robust Visual Tracking (2014, methodology): “Ours ASLA SCM Struck LSHT Figure 1: A comparison of our method with the state-of-the-art trackers ASLA [14], SCM [20], Struck [9] and LSHT [10] in challenging situations, namely partial occlusions, out-of-plane rotations and illumination variations.”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, neutral): “[227], [228], [229], [230], [231], [232], and [233].”
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking (2016, methodology): “We validate our Continuous Convolution Operator Tracker (C-COT) in a comprehensive comparison with 20 state-of-the-art methods: ASLA [25], TLD [26], Struck [21], LSHT [22], EDFT [14], DFT [41], CFLB [18], ACT [13], TGPR [19], KCF [24], DSST [9], SAMF [31], MEEM [47], DAT [40], LCT [36], HCF [35], St”
Adaptive Color Attributes for Real-Time Visual Tracking (2014, methodology): “The results of Struck [7], EDFT [6], CSK [9], LSHT [8] and our approach are represented by blue, grey, cyan, magenta and red boxes respectively.”
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].”
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”
Unveiling the Power of Deep Tracking (2018, methodology): “We compare our method with the following state-of-the-art methods: CCOT [7], ECO [1], MDNet [13], HDT [20], FCNT [12], SRDCF [18], DeepSRDCF [40], BACF [41], Staple [3], MEEM [4], HCF [8], SAMF [42], DSST [43], MUSTER [25], Struck [44], and ASLA [45].”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “, fragments voting based [1,9,5], incorporating spatial constraints between the parts [42,37], alignment-pooling across the local patches [14]; iv) and the widely followed trackingby-detection (or discriminative) methods [6,7,20,2,8,21,31,45], which treat the tracking problem as a classification tas”
Object Tracking Benchmark (2015, methodology): “7 ’12 LSHT [33] @ @ @ H @ @ @ @ 20 ’13 LSK [48] @ @ @ @ @ @ @ 5.”
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization (2014, methodology): “For a more complete comparison, we also include three more recent trackers in this experiment: LSHT [12], LSST [25] and SPLTT [23].”
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): “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], ”