Efficient correlation filter tracking with adaptive training sample update scheme

Jiang, Shan; Li, Shuxiao; Zhu, Chengfei; Yan, Nan

doi:10.1109/icpr48806.2021.9413005

Cited by 2 publications

(2 citation statements)

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“…Where ηy is a weighting coefficient. The ratio of peak score can be calculated as follows: max y t y r y  (12) It is noted that ry can enhance the color reliability for localizing the maximum peak position of the current response map. Based on ra and ry, the arithmetic mean value was calculated as a combined reliability score as follows:…”

Section: Calculating the Ratio Ra Of Apcementioning

confidence: 99%

“…It enhanced the robustness of correlation filtering based tracker. Then, the subsequent correlation filtering based trackers made improvements mainly from the viewpoints of feature selection, scale estimation [8,9], regularization [10,11] and model update [12]. For example, the typical features such as Histogram of Oriented Gradients (HOG) [13], or Color Names (CN) [14] were employed to describe objects more effectively.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Correlation Filtering Tracker Based on Joint Reliability Evaluation of Target Position Changes

Han

Liu

et al. 2023

J. Phys.: Conf. Ser.

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Visual tracking is an important branch in computer vision. In complex scenarios, there exist various interference factors, e.g. background clutter, similar objects etc., making robust tracking based on correlation filter algorithm still a challenging task. In this paper, a correlation filter algorithm based on a novel adaptive multi-cue fusion strategy was proposed. First, a unified response map evaluation strategy was presented to assess the tracking reliability by combing the average peak correlation energy and the response peak historical information. Second, according to the cue reliability, an adaptive multi-cue fusion strategy was proposed to adaptively fuse two tracking cues, correlation filter and color histogram. The experimental results on OTB-2013 and UAV123 demonstrated that the proposed algorithm achieved competitive performance to the state-of the-art trackers.

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Section: Calculating the Ratio Ra Of Apcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Correlation Filtering Tracker Based on Joint Reliability Evaluation of Target Position Changes

Han

Liu

et al. 2023

J. Phys.: Conf. Ser.

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Efficient and Practical Correlation Filter Tracking

Zhu

Jiang

et al. 2021

Sensors

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Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).

show abstract

Efficient correlation filter tracking with adaptive training sample update scheme

Cited by 2 publications

References 30 publications

A Robust Correlation Filtering Tracker Based on Joint Reliability Evaluation of Target Position Changes

A Robust Correlation Filtering Tracker Based on Joint Reliability Evaluation of Target Position Changes

Efficient and Practical Correlation Filter Tracking

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