Discriminative Correlation Filter with Channel and Spatial Reliability

Lukežič, Alan; Vojíř, Tomáš; Zajc, Luka Čehovin; Matas, Jiřı́; Kristan, Matej

doi:10.1109/cvpr.2017.515

Cited by 903 publications

(699 citation statements)

References 37 publications

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“…A pre-defined weighting window is employed to force the energy of the filters to concentrate in the central region of the search window, decreasing the number of distorted samples. A similar idea is developed in the background-aware correlation filter (BACF) [30] and the spatial reliability strategy (CSRDCF) [31]. The difference is that BACF utilises spatial sample pruning to reduce the boundary effect and CSRDCF exploits a colour histogram based foreground-background mask to filter out non-target region.…”

Section: Dcf Tracking Formulationsmentioning

confidence: 99%

“…However, this leads to spatial distortion. To mitigate this problem, spatial regularisation is introduced in the DCF paradigm via different techniques [11,31,30,12] to concentrate the filter energy in the centre region. As a result of this measure, the validity of the region encompassed by circular correlation increases, even with a large β.…”

Section: Objectivementioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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Section: Dcf Tracking Formulationsmentioning

confidence: 99%

Section: Objectivementioning

confidence: 99%

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

An accelerated correlation filter tracker

Xu¹,

Feng²,

Wu³

et al. 2020

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…As illustrated in Fig. 8, we compare the precision plots and success plots obtained by our OA-LSTM-ADA and several state-of-the-art trackers including MemTrack [50], TRACA [57], SiamFC-tri [38], CFNet2-tri [38], ACFN [58], CNN-SVM [59], DLSSVM [60], SiamFC [5], CFNet [9], CSR-DCF [61], Staple [30], RFL [10], KCF [29] and CNT [62]. We choose these methods because SiamFC, CFNet, SiamFC-tri and CFNet2-tri are Siamese network based tracking methods, which are closely related to our OA-LSTM-ADA (recall that OA-LSTM-ADA utilizes the Siamese network to pre-estimate the densely sampled proposals).…”

Section: Quantitative Comparisonmentioning

confidence: 99%

“…We compare our OA-LSTM-ADA with the top 9 trackers on the VOT-2017 real-time challenge, including CSR-DCF-plus [61], CSR-DCF-f [61], SiamFC [5], ECOhc [72], Staple [30], KFebT [73], ASMS [74], SSKCF and UCT [76]. Fig.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

Yan

Chen

et al. 2020

Neurocomputing

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In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matching-based tracking methods (such as Siamese networks) possess remarkable speed superiority.However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel real-time visual tracking method, which adopts an object-adaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matching-based tracking method to pre-estimate dense proposals and selects high-quality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the state-of-the-art performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.

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Autonomous Magnetic Navigation in Endoscopic Image Mosaics

Mattille,

Boehler,

Lussi

et al. 2024

Advanced Science

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Endoscopes navigate within the human body to observe anatomical structures with minimal invasiveness. A major shortcoming of their use is their narrow field‐of‐view during navigation in large, hollow anatomical regions. Mosaics of endoscopic images can provide surgeons with a map of the tool's environment. This would facilitate procedures, improve their efficiency, and potentially generate better patient outcomes. The emergence of magnetically steered endoscopes opens the way to safer procedures and creates an opportunity to provide robotic assistance both in the generation of the mosaic map and in navigation within this map. This paper proposes methods to autonomously navigate magnetic endoscopes to 1) generate endoscopic image mosaics and 2) use these mosaics as user interfaces to navigate throughout the explored area. These are the first strategies, which allow autonomous magnetic navigation in large, hollow organs during minimally invasive surgeries. The feasibility of these methods is demonstrated experimentally both in vitro and ex vivo in the context of the treatment of twin‐to‐twin transfusion syndrome. This minimally invasive procedure is performed in utero and necessitates coagulating shared vessels of twin fetuses on the placenta. A mosaic of the vasculature in combination with autonomous navigation has the potential to significantly facilitate this challenging surgery.

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Discriminative Correlation Filter with Channel and Spatial Reliability

Cited by 903 publications

References 37 publications

An accelerated correlation filter tracker

An accelerated correlation filter tracker

Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

Autonomous Magnetic Navigation in Endoscopic Image Mosaics

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