Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking

Zhu, Xuefeng; Wu, Xiao-Jun; Xu, Tianyang; Feng, Zhen-Hua; Kittler, Josef

doi:10.1109/tcsvt.2020.2979480

Cited by 45 publications

(9 citation statements)

References 64 publications

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“…An alternate description of visuals was proposed by Zhu et al [25]. From a target with rapid appearance changes, the dynamic appearance information is extracted by proposing the collaborative representation between successive frames specifically, this has the effect of reducing the background's undesirable impact.…”

Section: Related Workmentioning

confidence: 99%

Visual Object Tracking Based on Modified LeNet-5 and RCCF

Gullapelly¹,

Banik²

2023

Computer Systems Science and Engineering

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The field of object tracking has recently made significant progress. Particularly, the performance results in both deep learning and correlation filters, based trackers achieved effective tracking performance. Moreover, there are still some difficulties with object tracking for example illumination and deformation (DEF). The precision and accuracy of tracking algorithms suffer from the effects of such occurrences. For this situation, finding a solution is important. This research proposes a new tracking algorithm to handle this problem. The features are extracted by using Modified LeNet-5, and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method (RCCF). In Modified LeNet-5, the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution layers. The high-level, middle-level, and handcraft features are extracted from the modified LeNet-5 network. The handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual object. The LeNet features are more suitable for a target appearance change in object tracking. Extensive experiments were conducted by the Object Tracking Benchmarking (OTB) databases like OTB50 and OTB100. The experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 93.8% and 92.5%. The average running frame rate reaches 42 frames per second, which can meet the real-time requirements.

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Section: Related Workmentioning

confidence: 99%

Visual Object Tracking Based on Modified LeNet-5 and RCCF

Gullapelly¹,

Banik²

2023

Computer Systems Science and Engineering

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“…RGB tracking is the most fundamental sub-task in visual object tracking [15,16,22,23,24]. Among its numerous modelling techniques, trackers based on Siamese networks are the widely studied in the recent deep learning paradigm.…”

Section: Tracking With Single Modalitymentioning

confidence: 99%

Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking

Tang¹,

Xu²,

Li³

et al. 2022

Preprint

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We address the problem of multi-modal object tracking in video and explore various options of fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities including pixel-level, feature-level and decision-level fusion. Specifically, different from the existing methods, paradigm of image fusion task is heeded for fusion at pixel level. Featurelevel fusion is fulfilled by attention mechanism with channels excited optionally. Besides, at decision level, a novel fusion strategy is put forward since an effortless averaging configuration has shown the superiority. The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation. A variant of which produced the winning tracker at the Visual Object Tracking Challenge 2020 (VOT-RGBT2020). The concurrent exploration of innovative pixel-and feature-level fusion strategies highlights the advantages of the proposed decision-level fusion method. Extensive experimental results on three challenging datasets, i.e., GTOT, VOT-RGBT2019, and VOT-RGBT2020, demonstrate the effectiveness and robustness of the proposed method, compared to the state-of-the-art approaches. Code will be shared at https://github.com/Zhangyong-Tang/DFAT.

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“…It plays a significant role in various practical applications, including automatic driving, humancomputer interaction, intelligent surveillance, and so on [1], [2], [3]. Over the past decades, visual object tracking has achieved significant progress [4], [5], [6], [7]. Conventional visual object tracking mainly focus on RGB videos captured by a monocular camera.…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking on Multi-modal RGB-D Videos: A Review

Zhu¹,

Xu²,

Wu³

2022

Preprint

Self Cite

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The development of visual object tracking has continued for decades. Recent years, as the wide accessibility of the low-cost RGBD sensors, the task of visual object tracking on RGB-D videos has drawn much attention. Compared to conventional RGB-only tracking, the RGB-D videos can provide more information that facilitates objecting tracking in some complicated scenarios. The goal of this review is to summarize the relative knowledge of the research filed of RGB-D tracking. To be specific, we will generalize the related RGB-D tracking benchmarking datasets as well as the corresponding performance measurements. Besides, the existing RGB-D tracking methods are summarized in the paper. Moreover, we discuss the possible future direction in the field of RGB-D tracking.

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Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking

Cited by 45 publications

References 64 publications

Visual Object Tracking Based on Modified LeNet-5 and RCCF

Visual Object Tracking Based on Modified LeNet-5 and RCCF

Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking

Visual Object Tracking on Multi-modal RGB-D Videos: A Review

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