Multi-cue Correlation Filters for Robust Visual Tracking

By Ning Wang, Wen-gang Zhou, Qi Tian, Richang Hong, Meng Wang, Houqiang Li. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

402 citations recorded by the source corpus.

In recent years, many tracking algorithms achieve impressive performance via fusing multiple types of features, however, most of them fail to fully explore the context among the adopted multiple features and the strength of them. In this paper, we propose an efficient multi-cue analysis framework for robust visual tracking. By combining different types of features, our approach constructs multiple experts through Discriminative Correlation Filter (DCF) and each of them tracks the target independently. With the proposed robustness evaluation strategy, the suitable expert is selected for tracking in each frame. Furthermore, the divergence of multiple experts reveals the reliability of the current tracking, which is quantified to update the experts adaptively to keep them from corruption. Through the proposed multi-cue analysis, our tracker with standard DCF and deep features achieves outstanding results on several challenging benchmarks: OTB-2013, OTB-2015, Temple-Color and VOT 2016. On the other hand, when evaluated with only simple hand-crafted features, our method demonstrates comparable performance amongst complex non-realtime trackers, but exhibits much better efficiency, with a speed of 45 FPS on a CPU.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a5278fc76eff08668bc1957b01b22eb627fa2c36

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Citations in the corpus

AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization (2020, methodology): “Compared to deep trackers including ASRCF [31], TADT [51], SiameseFC [15], DSiam [56], MCCT [13], ADNet [16], CFNet [52], DeepSTRCF [14], UDT+ [53], HCF [18], C-COT [17], ECO [20], IBCCF [54], MCPF [19] and CREST [35], AutoTrack with a single CPU exhibits the”
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 ”
Unsupervised Deep Tracking (2019, neutral): “, [35, 40, 53, 7]).”
Is First Person Vision Challenging for Object Tracking? (2020, neutral): “Specifically, we have considered short-term trackers [46] based on correlation-filters with hand-crafted features (MOSSE [11], DSST [23], KCF [37], Staple [7], BACF [42], DCFNet [79], STRCF [49], MCCTH [78]) and deep features (ECO [21], ATOM [22], DiMP [9], PrDiMP [24], KYS [10]).”
STMTrack: Template-free Visual Tracking with Space-time Memory Networks (2021, neutral): “715 MCCT [49] 0.”
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].”
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].”
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”