scholarly journals Adaptive Model Update Strategy for Correlation Filter Trackers

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 151493-151505
Author(s):  
Zhuang He ◽  
Qi Li ◽  
Meng Chang ◽  
Huajun Feng ◽  
Zhihai Xu
Keyword(s):  
Correlation Filter ◽  
Adaptive Model ◽  
Model Update ◽  
Update Strategy

scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

A Robust Visual Tracker Based on DCF Algorithm

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1819-1834
Author(s):  
Menglei Jin ◽  
Weibin Liu ◽  
Weiwei Xing
Keyword(s):  
Linear Interpolation ◽  
Correlation Filter ◽  
Background Information ◽  
Video Object Tracking ◽  
Multiple Datasets ◽  
Tracking Algorithms ◽  
Current Correlation ◽  
Model Update ◽  
Global Background ◽  
Update Strategy

Since Correlation Filter appeared in the field of video object tracking, it is very popular due to its excellent performance. The Correlation Filter-based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness. However, there are still some fields for improvement in the Correlation Filter-based tracking algorithms. First, during the training of the classifier, the background information that can be utilized is very limited. Moreover, the introduction of the cosine window further reduces the background information. These reasons reduce the discriminating power of the classifier. This paper introduces more global background information on the basis of the DCF tracker to improve the discriminating ability of the classifier. Then, in some complex scenes, tracking loss is easy to occur. At this point, the tracker will be treated the background information as the object. To solve this problem, this paper introduces a novel re-detection component. Finally, the current Correlation Filter-based tracking algorithms use the linear interpolation model update method, which cannot adapt to the object changes in time. This paper proposes an adaptive model update strategy to improve the robustness of the tracker. The experimental results on multiple datasets can show that the tracking algorithm proposed in this paper is an excellent algorithm.

A robust correlation filter tracking method based on adaptive model updating

2021 ◽  
Author(s):  
Lin Zhang ◽  
Xingzhong Xiong ◽  
Xin Zeng ◽  
Ya Dong
Keyword(s):  
Model Updating ◽  
Correlation Filter ◽  
Adaptive Model ◽  
Tracking Method

scholarly journals An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking

2018 ◽  
Vol 8 (11) ◽  
pp. 2154 ◽  
Author(s):  
Xingmei Wang ◽  
Guoqiang Wang ◽  
Zhonghua Zhao ◽  
Yue Zhang ◽  
Binghua Duan
Keyword(s):  
Target Tracking ◽  
Adaptive Filter ◽  
Poor Performance ◽  
Image Sequences ◽  
Correlation Filter ◽  
Tracking Errors ◽  
The Poor ◽  
Continuous Scale ◽  
Underwater Target ◽  
Update Strategy

To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.

Visual tracking algorithm based on improved Bhattacharyya coefficient and model update strategy

2015 ◽  
Vol 36 (1) ◽  
pp. 52-57
Author(s):  
Huang An-qi ◽  
◽  
Hou Zhi-qiang ◽  
Yu Wang-sheng ◽  
Liu Xiang
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm ◽  
Bhattacharyya Coefficient ◽  
Model Update ◽  
Update Strategy

scholarly journals Robust Probabilistic Discriminative Model Prediction Tracker via Improved Model Update Strategy

2021 ◽  
Author(s):  
Shaolong Chen ◽  
Changzhen Qiu ◽  
Yurong Huang ◽  
Zhiyong Zhang
Keyword(s):  
Model Prediction ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Robust Model ◽  
Model Update ◽  
Discriminative Model ◽  
Background Clutter ◽  
Improved Model ◽  
Update Strategy ◽  
The Impact

Abstract In the visual object tracking, the tracking algorithm based on discriminative model prediction have shown favorable performance in recent years. Probabilistic discriminative model prediction (PrDiMP) is a typical tracker based on discriminative model prediction. The PrDiMP evaluates tracking results through output of the tracker to guide online update of the model. However, the tracker output is not always reliable, especially in the case of fast motion, occlusion or background clutter. Simply using the output of the tracker to guide the model update can easily lead to drift. In this paper, we present a robust model update strategy which can effectively integrate maximum response, multi-peaks and detector cues to guide model update of PrDiMP. Furthermore, we have analyzed the impact of different model update strategies on the performance of PrDiMP. Extensive experiments and comparisons with state-of-the-art trackers on the four benchmarks of VOT2018, VOT2019, NFS and OTB100 have proved the effectiveness and advancement of our algorithm.

scholarly journals An adaptive eco with weighted feature for visual tracking

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5139-5148
Author(s):  
Yan Zhou ◽  
Hongwei Guo ◽  
Dongli Wang ◽  
Chunjiang Liao
Keyword(s):  
Fuzzy Model ◽  
Visual Object ◽  
Adaptive Mechanism ◽  
Feature Maps ◽  
Visual Object Tracking ◽  
Tracking Process ◽  
Model Update ◽  
Fitting In ◽  
Update Strategy ◽  
Self Adaptive

The efficient convolution operator (ECO) have manifested predominant results in visual object tracking. However, in the pursuit of performance improvement, the computational burden of the tracker becomes heavy, and the importance of different feature layers is not considered. In this paper, we propose a self-adaptive mechanism for regulating the training process in the first frame. To overcome the over-fitting in the tracking process, we adopt the fuzzy model update strategy. Moreover, we weight different feature maps to enhance the tracker performance. Comprehensive experiments have conducted on the OTB-2013 dataset. When adopting our ideas to adjust our tracker, the self-adaptive mechanism can avoid unnecessary training iterations, and the fuzzy update strategy reduces one fifth tracking computation compared to the ECO. Within reduced computation, the tracker based our idea incurs less than 1% loss in AUC (area-undercurve).

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