Real-time part-based visual tracking via adaptive correlation filters

By Ting Liu, G. Wang, Qingxiong Yang. Computer Vision and Pattern Recognition, 2015

395 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6410c97ae03d356e14544c8e95f5367fb7ebb6e6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Qi Tian
5. Lei Zhang
6. Yi Yang
7. Radu Timofte
8. Wangmeng Zuo

Citations in the corpus

• Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking (2018, methodology): “To cope with the size change and occlusion, several scale-adaptive [11, 23, 24] and partbased trackers [26, 27] are further investigated.”
• 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].”
• CREST: Convolutional Residual Learning for Visual Tracking (2017, background): “The examples include kernelized correlation filters [17], multiple dimensional features [12, 18], context learning [49], scale estimation [7], re-detection [31], subspace learning [28], shortterm and long-term memory [20], reliable collection [27] and spatial regularization [9].”
• Online Multi-Expert Learning for Visual Tracking (2020, neutral): “In [34], a part-based tracker is proposed, where object parts are tracked by the KCF method [18] independently.”
• Kernel Cross-Correlator (2017, background): “In (Liu, Wang, and Yang 2015), part based KCF is proposed to address the problem of occlusion.”
• 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], [52], [53], [54], [55], [56].”
• 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].”