Visual tracking via dynamic saliency discriminative correlation filter

Gao, Lina; Liu, Bing; Fu, Ping; Xu, Mingzhu; Li, Junbao

doi:10.1007/s10489-021-02260-2

Cited by 22 publications

(3 citation statements)

References 55 publications

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“…One limitation of the DSST method is its poor performance to decide the target size when the object size changes frequently [41]. Regrading fDSST, the only improvement over DSST is the tracking speed [42].…”

Section: Plos Onementioning

confidence: 99%

A practical evaluation of correlation filter-based object trackers with new features

et al. 2022

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Visual object tracking is a critical problem in the field of computer vision. The visual object tracker methods can be divided into Correlation Filters (CF) and non-correlation filters trackers. The main advantage of CF-based trackers is that they have an accepted real-time tracking response. In this article, we will focus on CF-based trackers, due to their key role in online applications such as an Unmanned Aerial Vehicle (UAV), through two contributions. In the first contribution, we proposed a set of new video sequences to address two uncovered issues of the existing standard datasets. The first issue is to create two video sequence that is difficult to be tracked by a human being for the movement of the Amoeba under the microscope; these two proposed video sequences include a new feature that combined background clutter and occlusion features in a unique way; we called it hard-to-follow-by-human. The second issue is to increase the difficulty of the existing sequences by increasing the displacement of the tracked object. Then, we proposed a thorough, practical evaluation of eight CF-base trackers, with the top performance, on the existing sequence features such as out-of-view, background clutters, and fast motion. The evaluation utilized the well-known OTB-2013 dataset as well as the proposed video sequences. The overall assessment of the eight trackers on the standard evaluation metrics, e.g., precision and success rates, revealed that the Large Displacement Estimation of Similarity transformation (LDES) tracker is the best CF-based tracker among the trackers of comparison. On the contrary, with a deeper analysis, the results of the proposed video sequences show an average performance of the LDES tracker among the other trackers. The eight trackers failed to capture the moving objects in every frame of the proposed Amoeba movement video sequences while the same trackers managed to capture the object in almost every frame of the sequences of the standard dataset. These results outline the need to improve the CF-based object trackers to be able to process sequences with the proposed feature (i.e., hard-to-follow-by-human).

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Section: Plos Onementioning

confidence: 99%

A practical evaluation of correlation filter-based object trackers with new features

et al. 2022

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“…GradNet [3] 0.639 0.861 UpdateNet [23] 0.647 0.861 ATOM [22] 0.671 0.882 MDNet [27] 0.678 0.909 DSDCF [39] 0.667 0.784 DiMP [2] 0.688 0.900 ECO [5] 0.691 0.910 DaSiamRPN [4] 0.658 0.880 SiamFC [6] 0.587 0.722 SiamFC++ [40] 0.682 0.896 SiamBAN [17] 0.696 0.910 SiamRPN++ [12] 0.696 0.915 SiamRCNN [41] 0.700 0.891 DROL-RPN [42] 0.715 0.934 Ours 0.707 0.922…”

Section: Succ Precmentioning

confidence: 99%

SiamOT: An Improved Siamese Network with Online Training for Visual Tracking

Gong

Zhou

Zhang

2022

Sensors

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As a prevailing solution for visual tracking, Siamese networks manifest high performance via convolution neural networks and weight-sharing schemes. Most existing Siamese networks have adopted various offline training strategies to realize precise tracking by comparing the extracted target features with template features. However, their performances may degrade when dealing with unknown targets. The tracker is unable to learn background information through offline training, and it is susceptible to background interference, which finally leads to tracking failure. In this paper, we propose a twin-branch architecture (dubbed SiamOT) to mitigate the above problem in existing Siamese networks, wherein one branch is a classical Siamese network, and the other branch is an online training branch. Especially, the proposed online branch utilizes feature fusion and attention mechanism, which is able to capture and update both the target and the background information so as to refine the description of the target. Extensive experiments have been carried out on three mainstream benchmarks, along with an ablation study, to validate the effectiveness of SiamOT. It turns out that SiamOT achieves superior performance with stronger target discrimination abilities.

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“…The discriminative correlation filter (DCF)-based tracker in [15] mitigated two major problems in the existing paradigm: the spatial boundary effect and temporal filter degeneration. The efficient appearance learning models in DCF have been proven to be effective in visual tracking [16]. The spatially regularized DCF (SRDCF) [17] method learned filters from training examples with rigid spatial constraints.…”

Section: Introductionmentioning

confidence: 99%

Variational Online Learning Correlation Filter for Visual Tracking

Wang,

Liu,

Deng

2024

Mathematics

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Recently, discriminative correlation filters (DCF) have been successfully applied for visual tracking. However, traditional DCF trackers tend to separately solve boundary effect and temporal degradation problems in the tracking process. In this paper, a variational online learning correlation filter (VOLCF) is proposed for visual tracking to improve the robustness and accuracy of the tracking process. Unlike previous methods, which use only first-order temporal constraints, this approach leads to overfitting and filter degradation. First, beyond the standard filter training requirement, our proposed VOLCF method introduces a model confidence term, which leverages the temporal information of adjacent frames during filter training. Second, to ensure the consistency of the temporal and spatial characteristics of the video sequence, the model introduces Kullback–Leibler (KL) divergence to obtain the second-order information of the filter. In contrast to traditional target tracking models that rely solely on first-order feature information, this approach facilitates the acquisition of a generalized connection between the previous and current filters. As a result, it incorporates joint-regulated filter updating. Through quantitative and qualitative analyses of the experiment, it proves that the VOLCF model has excellent tracking performance.

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Visual tracking via dynamic saliency discriminative correlation filter

Cited by 22 publications

References 55 publications

A practical evaluation of correlation filter-based object trackers with new features

A practical evaluation of correlation filter-based object trackers with new features

SiamOT: An Improved Siamese Network with Online Training for Visual Tracking

Variational Online Learning Correlation Filter for Visual Tracking

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