Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

Wang, Wenbin; Liu, Chao; Xu, Bo; Li, Long; Chen, Wei; Tian, Yingzhong

doi:10.3390/app10062151

Cited by 2 publications

(1 citation statement)

References 42 publications

(75 reference statements)

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“…Although LMCF [26] put forward an approach to prevent model drift through appraising the confidence of the tracking result, it can not comprehensively evaluate scale-rotation and translation changes. In the paper [27,28], although the scale estimation problems can be solved by a scale pyramid representation and the model drift is overcome by a high-confidence judgement mechanism, the method is limited to solve the rotation estimation problems. Based on the above discussion we can see that these methods have both advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking using Surface Fitting for Scale and Rotation Estimation

2021

KSII TIIS

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Since correlation filter appeared in the field of object tracking, it plays an increasingly vital role due to its excellent performance. Although many sophisticated trackers have been successfully applied to track the object accurately, very few of them attaches importance to the scale and rotation estimation. In order to address the above limitation, we propose a novel method combined with Fourier-Mellin transform and confidence evaluation strategy for robust object tracking. In the first place, we construct a correlation filter to locate the target object precisely. Then, a log-polar technique is used in the Fourier-Mellin transform to cope with the rotation and scale changes. In order to achieve subpixel accuracy, we come up with an efficient surface fitting mechanism to obtain the optimal calculation result. In addition, we introduce a confidence evaluation strategy modeled on the output response, which can decrease the impact of image noise and perform as a criterion to evaluate the target model stability. Experimental experiments on OTB100 demonstrate that the proposed algorithm achieves superior capability in success plots and precision plots of OPE, which is 10.8% points and 8.6% points than those of KCF. Besides, our method performs favorably against the others in terms of SRE and TRE validation schemes, which shows the superiority of our proposed algorithm in scale and rotation evaluation.

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Section: Introductionmentioning

confidence: 99%

Visual Object Tracking using Surface Fitting for Scale and Rotation Estimation

2021

KSII TIIS

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Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection

Yuan

Liu

2021

Applied Sciences

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The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects—first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.

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Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

Cited by 2 publications

References 42 publications

Visual Object Tracking using Surface Fitting for Scale and Rotation Estimation

Visual Object Tracking using Surface Fitting for Scale and Rotation Estimation

Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection

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