Hedged Deep Tracking

By Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, Ming-Hsuan Yang. 2016

677 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Learned in Translation: Contextualized Word Vectors (2017, methodology): “Qi et al. [2016] propose a CNN-based object tracking framework, which uses hierarchical features from a pretrained CNN (VGG-19 by Simonyan and Zisserman [2014]).”
Attentional Correlation Filter Network for Adaptive Visual Tracking (2017, methodology): “Similarly, for correlation filter based trackers, only some of the convolutional features are useful at a time [6,8,26,30].”
A Twofold Siamese Network for Real-Time Object Tracking (2018, background): “Some trackers [7, 26, 30, 9, 6] integrate deep features into conventional tracking approaches and benefit from the expressive power of CNN features.”
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 ”
Learning Dynamic Memory Networks for Object Tracking (2018, methodology): “To further show our tracking accuracy, we also compared with another 8 recent state-of-the art trackers that are not real-time speed, including CREST [26], CSR-DCF [19], MCPF [38], SRDCFdecon [9], SINT [29], SRDCF [6], HDT [23], HCF [20] on OTB-2015.”
Multi-cue Correlation Filters for Robust Visual Tracking (2018, methodology): “[38] propose a HDT algorithm that fuses several DCFs through adaptive hedged method.”
CREST: Convolutional Residual Learning for Visual Tracking (2017, methodology): “We compare our CREST tracker with 29 trackers from the OTB-2013 benchmark [45] and other 21 state-of-the-art trackers including KCF [17], MEEM [48], TGPR [13], DSST [7], RPT [27], MUSTer [20], LCT [31], HCFT [30], FCNT [42], SRDCF [9], CNN-SVM [19], DeepSRDCF [8], DAT [35], Staple [2], SRDCFdecon [1”
Unveiling the Power of Deep Tracking (2018, methodology): “We compare our method with the following state-of-the-art methods: CCOT [7], ECO [1], MDNet [13], HDT [20], FCNT [12], SRDCF [18], DeepSRDCF [40], BACF [41], Staple [3], MEEM [4], HCF [8], SAMF [42], DSST [43], MUSTER [25], Struck [44], and ASLA [45].”
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (2017, methodology): “ADNet-fast, the fast version of ADNet, has a 3% 2We evaluated the GOTURN [12] on the OTB dataset using the author’s code. performance degradation but runs in real-time (15 fps) and shows performance comparable with those of other CNNbased trackers such as DeepSRDCF [8] and HDT [25].”
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking (2018, background): “The comparison with 15 state-of-the-art trackers (SiamRPN [23], DaSiamRPN [59], TRACA [6], ACT [4], BACF [13], ECOHC [7], CREST [42], SiamFC [2], Staple [1], PTAV [11], SINT [45], CFNet [46], SA-Siam [17], HDT [36] and HCFT [32]) is shown in Fig.”
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking (2018, methodology): “And more discriminative features are widely used, such as HOG [8], color names (CN) [14] and deep CNN features [28, 34].”
Online Multi-Expert Learning for Visual Tracking (2020, neutral): “Different from HDT, our multiple experts tracking framework considers the models from previous frames while HDT only considers the different layers in the current frame.”
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking (2017, methodology): “CFs [10, 24, 8, 29], the updated deep trackers aim to learn from scratch the target-specific features for each new video.”
Large Margin Object Tracking with Circulant Feature Maps (2017, methodology): “Recent studies [27, 21, 28, 23, 15] have shown state-of-the-art re-sults on many object tracking benchmarks.”
Target-Aware Deep Tracking (2019, background): “Deep features from multiple layers have also integrated for visual tracking [10, 32, 7, 3].”
Unsupervised Deep Tracking (2019, neutral): “, [35, 40, 53, 7]).”