Multi-task Correlation Particle Filter for Robust Object Tracking

By Tianzhu Zhang, Changsheng Xu, Ming-Hsuan Yang. Computer Vision and Pattern Recognition, 2017

403 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/d34ab5269af57e3f57c7b8e41ad5bb51b82cd49f

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Citations in the corpus

Multi-cue Correlation Filters for Robust Visual Tracking (2018, methodology): “In HCF [31] or other methods [56, 38] that follow such a fusion strategy, the initial weight of high-level features is usually high such that semantic features play the dominant role in general.”
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking (2019, methodology): “The introduction of correlation filter methods [7, 28, 17, 9, 36, 34] make the tracking reach an unprecedented height both in efficiency and accuracy.”
Triplet Loss in Siamese Network for Object Tracking (2018, methodology): “More recently, the pre-trained deep networks are applied to extract feature from raw image for improving accuracy, such as DeepSRDCF [6], CCOT [7], MCPF [36], and ECO [4].”
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.”
Real-Time MDNet (2018, methodology): “Recent techniques based on discriminative correlation filters boost accuracy significantly by incorporating representations from deep neural networks [16, 17, 3, 4, 18].”
The Visual Object Tracking VOT2017 Challenge Results (2017, background): “The reader is referred to [83] for details.”
Target-Aware Deep Tracking (2019, methodology): “The proposed algorithm is compared with the state-of-the-art trackers, including the correlation ﬁlters based trackers, such as SRDCF [9], Staple [1], MCPF [48], CCOT [10], ECO [7], BACF [17], DRT [38], STRCF [22], and MCCT-H [43]; the non-real-time deep trackers such as MDNet [29], CREST [35], LSAR”
Deep Learning for Visual Tracking: A Comprehensive Survey (2019, methodology): “25 CM DCPF [85] VGG-19 Conv3-4, Conv4-4, Conv5-4 Still images ImageNet DAF N/A N/A N/A N/A CM MCPF [89] VGG-19 Conv3-4, Conv4-4, Conv5-4 Still images ImageNet DAF Intel 3.”
Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking (2019, methodology): “The results are compared with 20 state-of-the-art trackers with both hand-crafted based trackers and deep-based trackers, i.e., KCF [13], DSST [8], SAMF [18], MUSTER [14], BACF [15], SRDCF [7], STAPLE CA [21], MCCT-H [27], STRCF [16], ECO-HC (with gray-scale) [5], ECO [5], CCOT [9], HCF [19], ADNet ”
Res2Net: A New Multi-Scale Backbone Architecture (2019, background): “…it is of critical importance to design good features for multi-scale stimuli for visual cognition tasks, including image classiﬁcation [22], object detection [33], attention prediction [35], target tracking [50], action recognition [36], semantic segmentation [3], salient object detection [18].”
Unveiling the Power of Deep Tracking (2018, methodology): “[21] combines the deep DCF with a particle filter.”
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 [”
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters (2019, methodology): “The compared trackers include LSART [31], ECO [8], CFCF [16], GNet, MCCT [34], C-COT [13], C-SRDCF [28], SiamDCF, MCPF [37] and CRT [6].”