Long-term correlation tracking

By Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang. Computer Vision and Pattern Recognition, 2015

930 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/754504cf01ef3846259783e748b1d3ea52fa2c81

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking (2018, methodology): “Besides, long-term tracking [29], continuous convolution [15] and particle filter based methods [42] are also developed to improve the tracking accuracy and robustness.”
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking (2018, background): “, PTAV [13], LCT [38], MEEM [56] and TLD [24]), sparse trackers (e.”
Distractor-aware Siamese Networks for Visual Object Tracking (2018, methodology): “Ma et al. [20]propose a combination of KCF tracker and a random ferns classiﬁer as a detector that is used to correct the tracker.”
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].”
Multi-cue Correlation Filters for Robust Visual Tracking (2018, methodology): “enhanced by using multi-dimensional features [16, 26, 31], part-based strategies [29, 28], adaptive scale [9, 26], longterm framework [32, 17, 45], end-to-end learning [41, 40] and improved filter training methods [20, 30, 33, 14].”
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 [”
Kernel Cross-Correlator (2017, methodology): “An online random fern classiﬁer is employed as a re-detection component for the long-term correlation tracking (LCT) (Ma et al. 2015b).”
RGB-T Object Tracking: Benchmark and Baseline (2018, methodology): “idates, increase false positives, and reduce the tracking reliability. Therefore, we decompose the problem of target state estimation into two subproblems: translation estimation and scale estimation [60,61] as follows. 3) Translation estimation: In this work, we combine object representations of mu”
Multi-task Correlation Particle Filter for Robust Object Tracking (2017, methodology): “Correlation filters have recently been introduced into visual tracking and shown to achieve high speed as well as robust performance [4, 9, 16, 15, 18, 26, 24, 21, 25, 29].”
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark (2020, background): “, SPLT [86], LTMU [13], PTAV [25], LCT [63], MEEM [89] and TLD [44]), sparse trackers (e.”
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, methodology): “In addition, DCF-based tracking methods have also been extended to support long-term memory [46], multi-kernel method [47], structural constraints [48], support vector representation [49], sparse representation [50] and enhanced robustness [51]–[56].”
A Twofold Siamese Network for Real-Time Object Tracking (2018, methodology): “Tracker SA-Siam BACF PTAV ECOhc DSiamM EAST Staple SiamFC CFNet LMCF LCT (ours) [17] [10] [6] [11] [15] [2] [3] [38] [34] [27]”
Attentional Correlation Filter Network for Adaptive Visual Tracking (2017, background): “Correlation filter based trackers: Correlation filter based approaches have recently become increasingly popular due to the rapid speed of correlation filter calculations [2,3,5,7,16,18,27].”
The Visual Object Tracking VOT2016 Challenge Results (2016, methodology): “Firstly, the long-term correlation tracker (LCT) [68] is employed to determine the bounding box of the target at the current frame.”
ECO: Efficient Convolution Operators for Tracking (2016, methodology): “The recent advancement in DCF based tracking performance is driven by the use of multi-dimensional features [13, 15], robust scale estimation [7, 11], non-linear kernels [20], long-term memory components [28], sophisticated learning models [3, 10] and reducing boundary effects [9, 16].”
Online Multi-Expert Learning for Visual Tracking (2020, neutral): “Apart from the trackers used on the OTB2013 benchmark, we add another state-of-art trackers including CNN-SVM [63], LCT [64] and TGPR [65].”
Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals (2016, neutral): “SCM [59] Struck [20] TLD [26] ASLA [25] CXT [12] VTD [30] VTS [31] TGPR [17] RPT [34] [52] [35] Ours”
Staple: Complementary Learners for Real-Time Tracking (2015, positive): “The Long-term Correlation Tracker (LCT) [30] augments a standard Correlation Filter tracker with an additional Correlation Filter for conﬁdence estimation and a random forest for re-detection, both of which are only updated in conﬁdent frames.”
Context-Aware Correlation Filter Tracking (2017, background): “The work of [22] performs long term-tracking that is robust to appearance variation by correlating temporal context and training an online random fern classifier for re-detection.”
Fully-Convolutional Siamese Networks for Object Tracking (2016, methodology): “In addition to the trackers reported by [11], in Figure 3 we also compare against seven more recent state-of-the-art trackers presented in the major computer vision conferences and that can run at frame-rate speed: Staple [33], LCT [34], CCT [35], SCT4 [36], DLSSVM NU [37], DSST [38] and KCFDP [39].”