Parallel Correlation Filters for Real-Time Visual Tracking

Yang, Yijin; Zhang, Yihong; Li, Demin; Wang, Zhijie

doi:10.3390/s19102362

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…The experiments clearly show that the proposed tracker can effectively suppress the drift caused by tracking failure, which makes the tracking effect more robust. For the purpose of verifying that the proposed tracker can track the complex marine environmental objects with better performance, we conduct a comparative experiment on the complex marine environmental objects to compare the proposed tracker with the current representative trackers, including the PCF tracker [11], ECO-HC tracker [9], Staple tracker [30] and CSK tracker [5].…”

Section: Experimental Results and Analysis In The Complex Marine Environmentmentioning

confidence: 99%

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Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

Zhang

et al. 2020

Sensors

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Marine object tracking is critical for search and rescue activities in the complex marine environment. However, the complex marine environment poses a huge challenge to the effect of tracking, such as the variability of light, the impact of sea waves, the occlusion of other ships, etc. Under these complex marine environmental factors, how to design an efficient dynamic visual tracker to make the results accurate, real time and robust is particularly important. The parallel three-branch correlation filters for complex marine environmental object tracking based on a confidence mechanism is proposed by us. The proposed tracker first detects the appearance change and position change of the object by constructing parallel three-branch correlation filters, which enhances the robustness of the correlation filter model. Through the weighted fusion of response maps, the center position of the object is accurately located. Secondly, the Gaussian-triangle joint distribution is used to replace the original Gaussian distribution in the training phase. Finally, a verification mechanism of confidence metric is embedded in the filter update section to analyze the tracking effect of the current frame, and to update the filter sample from verification result. Thus, a more accurate correlation filter is trained to prevent model drift and achieve a good tracking effect. We found that the effect of various interferences on the filter is effectively reduced by comparing with other trackers. The experiments prove that the proposed tracker can play an outstanding role in the complex marine environment.

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Section: Experimental Results and Analysis In The Complex Marine Environmentmentioning

confidence: 99%

“…In addition, the use of a smooth Gaussian distribution as the expected output will make the positioning inaccurate [ 10 ]. Subsequently, the PCF [ 11 ] tracker came into being. Compared with the ECO_HC tracker on the video sequences, precision accuracy and overlap accuracy of the PCF tracker are better than the former.…”

Section: Introductionmentioning

confidence: 99%

Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

Zhang

et al. 2020

Sensors

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“…It is suggested that digital NCCs be implemented by efficient algorithms and hardware structures for decreasing their high multiplication complexity [35]. With the assist of fast computation for first-order moment, this paper presents an algorithm and a systolic array for fast NCCs that aim to reduce multiplication as much as possible.…”

Section: Discussionmentioning

confidence: 99%

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

Pan

Xia

et al. 2020

Sensors

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Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.

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“…Bertinetto et al [16] presented sum of template and pixel-wise learners (STAPLE) method using HOG and color histograms for target representation. More recently, Yijin et al [17] proposed Parallel Correlation Filters, Zhaohui et al [18] presented a tracker using correlation filter fused with color histogram and Hao et al [19] proposed a correlation filter-based tracker using enhanced features and adaptive Kalman filter.…”

Section: Related Workmentioning

confidence: 99%

Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter

Shin

Kim

et al. 2020

Applied Sciences

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Object tracking has long been an active research topic in image processing and computer vision fields with various application areas. For practical applications, the object tracking technique should be not only accurate but also fast in a real-time streaming condition. Recently, deep feature-based trackers have been proposed to achieve a higher accuracy, but those are not suitable for real-time tracking because of an extremely slow processing speed. The slow speed is a major factor to degrade tracking accuracy under a real-time streaming condition since the processing delay forces skipping frames. To increase the tracking accuracy with preserving the processing speed, this paper presents an improved kernelized correlation filter (KCF)-based tracking method that integrates three functional modules: (i) tracking failure detection, (ii) re-tracking using multiple search windows, and (iii) motion vector analysis to decide a preferred search window. Under a real-time streaming condition, the proposed method yields better results than the original KCF in the sense of tracking accuracy, and when a target has a very large movement, the proposed method outperforms a deep learning-based tracker, such as multi-domain convolutional neural network (MDNet).

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Parallel Correlation Filters for Real-Time Visual Tracking

Cited by 10 publications

References 39 publications

Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

Parallel Three-Branch Correlation Filters for Complex Marine Environmental Object Tracking Based on a Confidence Mechanism

The Algorithm and Structure for Digital Normalized Cross-Correlation by Using First-Order Moment

Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter

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