Context-Aware Correlation Filter Tracking

By Matthias Mueller, Neil G. Smith, Bernard Ghanem. Computer Vision and Pattern Recognition, 2017

607 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8c2ea6e7063199bd2d7403abd70418d5cffad6a4

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In Computer Vision and Pattern Recognition

Citations in the corpus

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].”
Target-Aware Deep Tracking (2019, background): “On the other hand, numerous trackers [16, 6, 28, 17, 35, 47, 12] are developed by improving the decision models including support vector machines, correlation filters, deep classifiers, and deep regressors.”
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking (2021, background): “Discriminative correlation filter (DCF) tackles the visual tracking by solving the ridge regression in Fourier domain, which exhibits attractive efficiency [20, 35, 34, 15, 37, 53, 11, 8].”
The Visual Object Tracking VOT2017 Challenge Results (2017, methodology): “The key difference to the original tracker is that MOSSE CA explicitly incorporates global context according a recent framework for CF trackers [46].”
Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking (2019, methodology): “, 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 [29], CFNet [25], CREST [23], MCPF [30], SINT [24], SiamFC [1], and HDT [22].”
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers (2018, background): “Many hard cases have been tackled by using context information [55, 38], short and long-term memory [35, 23], and scale-estimation [10], just to name a few.”
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications (2017, methodology): “These are SAMF [23], SRDCF [7], MEEM [55], C-COT [8] and MOSSE CA [34].”
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, methodology): “State-of-the-art competitors: We compare our proposed tracking method with 13 state-of-the-art trackers, including Staple [42], SRDCF [13], KCF [8], CSRDCF [14], STAPLE CA [82], BACF [15], C-COT [58], ECO [59],”
Deep Affinity Network for Multiple Object Tracking (2018, methodology): “The techniques for this task either track a single object in videos, thereby called Single Object Trackers [4]; or multiple objects simultaneously, hence termed Multiple Object Trackers [3].”
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization (2020, methodology): “Tracker AutoTrack ARCF-HC[26] ECO-HC[20] ARCF-H[26] STRCF[14] MCCT-H[13] STAPLE CA[10] BACF[22] CSR-DCF[30] SRDCF[8] Precision 72.”
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking (2018, background): “…Staple [3]) or deep features ( e.g ., HCFT [37] and ECO [7]) and regularization techniques ( e.g ., BACF [15], SRDCF [10], CSRDCF [36], Staple CA [40] and STRCF [29]), ensemble trackers ( e.g ., PTAV [13], LCT [38], MEEM [56] and TLD [24]), sparse trackers ( e.g ., L1APG [2] and ASLA [23]),…”
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking (2018, methodology): “[13], BACF [20], ECO-HC [9], SRDCFDecon [10], Staple [1], Staple+CA[30], SAMF+AT [3], DSST [11], SAMF [24], MEEM [41] and KCF [19]) and using CNN features (i.”
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (2018, background): “Staple-CA performs well on the SO and IV attributes, as its improved model update strategy can reduce over-ﬁtting to recent samples.”
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild (2018, methodology): “We chose among the following trackers: ECO [12], CSRDCF [44], BACF [30], SiameseFC [14], STAPLE CA [31], STAPLE [26], SRDCF [45], SAMF [46], CSK [47], KCF [48], DCF [48] and MOSSE [23].”