CREST: Convolutional Residual Learning for Visual Tracking

Song, Yibing; Ma, Chao; Gong, Lijun; Zhang, Jiawei; Lau, Rynson W. H.; Yang, Ming–Hsuan

doi:10.1109/iccv.2017.279

Cited by 530 publications

(396 citation statements)

References 45 publications

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“…Additionally, spatial-temporal context [86] and kernel tricks [27] are used to improve the learning formulation with the consideration of local appearance and nonlinear metric, respectively. The DCF paradigm has further been extended by exploiting scale detection [41,14,16], structural patch analysis [42,46,45], multi-clue fusion [71,50,28,4,72], sparse representation [88,90], support vector machine [75,92], enhanced sampling mechanisms [89,54] and end-to-end deep neural networks [73,67].…”

Section: Related Workmentioning

confidence: 99%

“…We evaluated the proposed method on several wellknown benchmarks, including OTB2013/OTB2015 [81,82], VOT2017/VOT2018 [33,34] and TrackingNet Test dataset [55], and compared it with a number of state-of-theart trackers, such as VITAL [68], MetaT [58], ECO [13], MCPF [89], CREST [67], BACF [31], CFNet [73], CACF [54], ACFN [11], CSRDCF [49], C-COT [51], Staple [4], SiamFC [5], SRDCF [15], KCF [27], SAMF [41], DSST [16] and other advanced trackers in VOT challenges, i.e., CFCF [23], CFWCR [25], LSART [69], UPDT [6], SiamRPN [91], MFT [34] and LADCF [83].…”

Section: Implementation and Evaluation Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

Feng

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

196

118

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We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perform group feature selection across both channel and spatial dimensions, thus to pinpoint the structural relevance of multi-channel features to the filtering system. In contrast to the widely used spatial regularisation or feature selection methods, to the best of our knowledge, this is the first time that channel selection has been advocated for DCF-based tracking. We demonstrate that our GFS-DCF method is able to significantly improve the performance of a DCF tracker equipped with deep neural network features. In addition, our GFS-DCF enables joint feature selection and filter learning, achieving enhanced discrimination and interpretability of the learned filters.To further improve the performance, we adaptively integrate historical information by constraining filters to be smooth across temporal frames, using an efficient lowrank approximation. By design, specific temporal-spatialchannel configurations are dynamically learned in the tracking process, highlighting the relevant features, and alleviating the performance degrading impact of less discriminative representations and reducing information redundancy. The experimental results obtained on OTB2013, OTB2015, VOT2017, VOT2018 and TrackingNet demonstrate the merits of our GFS-DCF and its superiority over the state-of-the-art trackers. The code is publicly available at https://github.com/XU-TIANYANG/ GFS-DCF.

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Section: Related Workmentioning

confidence: 99%

Section: Implementation and Evaluation Settingsmentioning

confidence: 99%

Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

Feng

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

196

118

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“…Wang et al [36] train two separate convolutional layers to regress Gaussian maps with the initial frame and update these layers every few frames. Similarly, Song et al [32] also utilize a number of gradient descent iterations in initialization and online update procedures. These trackers need many training iterations to capture the appearance variations of the target, which makes the tracker less effective and far from real-time requirements.…”

Section: Model Updating In Trackingmentioning

confidence: 99%

“…There are two groups of deep-learning-based trackers. The first group [36,28,32,4] improves the discriminative ability of deep networks by frequent online update. They utilize the first frame to initialize the model and update it * Corresponding Author: Dr. Dong Wang Figure 1.…”

Section: Introductionmentioning

confidence: 99%

GradNet: Gradient-Guided Network for Visual Object Tracking

Chen

Ouyang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

318

128

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The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feature and the performance totally relies on the general matching ability of the siamese network. However, this manner cannot capture the temporal variations of targets or background clutter. In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations. To be specific, the algorithm can utilize the information from the gradient to update the template in the current frame. In addition, a template generalization training method is proposed to better use gradient information and avoid overfitting. To our knowledge, this work is the first attempt to exploit the information in the gradient for template update in siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than other state-of-the-art trackers. The source codes are available at

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“…The speed of a tracking algorithm is measured in Frames Per Second (FPS). We compare our ACFT with a number of state-ofthe-art DCF trackers, including MetaT [48] (ECCV18), MCPF [49] (CVPR17), CREST [50] (ICCV17), BACF [30] (ICCV17), CFNet [51] (CVPR17), STA-PLE_CA [52] (CVPR17), ACFN [53] (CVPR17), CSRDCF [31] (CVPR17), C-COT [45] (ECCV16), Staple [27] (CVPR16), SRDCF [11] (ICCV15), KCF [43] (TPAMI15), SAMF [54] (ECCVW14) and DSST [55] (TPAMI17). Location error threshold The VOT2017 benchmark consists of 60 challenging video sequences.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

An accelerated correlation filter tracker

Xu¹,

Feng²,

Wu³

et al. 2020

Pattern Recognition

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Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features. Despite the outstanding performance achieved by the above combination, existing advanced trackers suffer from the burden of high computational complexity of the deep feature extraction and online model learning. We propose an accelerated ADMM optimisation method obtained by adding a momentum to the optimisation sequence iterates, and by relaxing the impact of the error between DCF parameters and their norm. The proposed optimisation method is applied to an innovative formulation of the DCF design, which seeks the most discriminative spatially regularised feature channels. A further speed up is achieved by an adaptive initialisation of the filter optimisation process. The significantly increased convergence of the DCF filter is demonstrated by establishing the optimisation process equivalence with a continuous dynamical system for which the convergence properties can readily be derived. The experimental results obtained on several well-known benchmarking datasets demonstrate the efficiency and robustness of the proposed ACFT method, with a tracking accuracy comparable to the start-of-the-art trackers.

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CREST: Convolutional Residual Learning for Visual Tracking

Cited by 530 publications

References 45 publications

Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

GradNet: Gradient-Guided Network for Visual Object Tracking

An accelerated correlation filter tracker

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