scholarly journals Long-Term Target Tracking of UAVs Based on Kernelized Correlation Filter

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3006
Author(s):  
Junqiang Yang ◽  
Wenbing Tang ◽  
Zuohua Ding
Keyword(s):  
Target Tracking ◽  
Template Matching ◽  
Control Method ◽  
Target Position ◽  
Correlation Filter ◽  
Background Information ◽  
The Status ◽  
Tracking Model ◽  
Update Strategy

During the target tracking process of unmanned aerial vehicles (UAVs), the target may disappear from view or be fully occluded by other objects, resulting in tracking failure. Therefore, determining how to identify tracking failure and re-detect the target is the key to the long-term target tracking of UAVs. Kernelized correlation filter (KCF) has been very popular for its satisfactory speed and accuracy since it was proposed. It is very suitable for UAV target tracking systems with high real-time requirements. However, it cannot detect tracking failure, so it is not suitable for long-term target tracking. Based on the above research, we propose an improved KCF to match long-term target tracking requirements. Firstly, we introduce a confidence mechanism to evaluate the target tracking results to determine the status of target tracking. Secondly, the tracking model update strategy is designed to make the model suffer from less background information interference, thereby improving the robustness of the algorithm. Finally, the Normalized Cross Correlation (NCC) template matching is used to make a regional proposal first, and then the tracking model is used for target re-detection. Then, we successfully apply the algorithm to the UAV system. The system uses binocular cameras to estimate the target position accurately, and we design a control method to keep the target in the UAV’s field of view. Our algorithm has achieved the best results in both short-term and long-term evaluations of experiments on tracking benchmarks, which proves that the algorithm is superior to the baseline algorithm and has quite good performance. Outdoor experiments show that the developed UAV system can achieve long-term, autonomous target tracking.

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.

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.

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.

scholarly journals Improved Hierarchical Convolutional Features for Robust Visual Object Tracking

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinping Sun
Keyword(s):  
Object Tracking ◽  
Target Position ◽  
Feature Representation ◽  
Correlation Filter ◽  
Low Rank ◽  
Visual Object ◽  
Threshold Condition ◽  
Current Frame ◽  
Visual Object Tracking

The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.

scholarly journals Scale-Adaptive Context-Aware Correlation Filter with Output Constraints for Visual Target Tracking

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jingxiang Xu ◽  
Xuedong Wu ◽  
Zhiyu Zhu ◽  
Kaiyun Yang ◽  
Yanchao Chang ◽  
...  
Keyword(s):  
Target Tracking ◽  
Correlation Filter ◽  
Context Aware ◽  
Tracking Accuracy ◽  
Large Area ◽  
Posterior Probability Distribution ◽  
Output Response ◽  
Tracking Process ◽  
Output Constraints ◽  
Tracking Model

Context-aware correlation filter tracker is one of the most advanced target trackers, and it has significant improvement in tracking accuracy and success rate compared with traditional trackers. However, because the complexity of background in the process of tracking can lead to inaccurate output response of target tracking, an accurate tracking model is difficult to be established. Moreover, the drift problem is easy to occur during the tracking process due to the imprecise tracking model, especially when the target has large area occlusion, fast motion, and deformation. Aiming at the drift problem in the target tracking process, a novel algorithm is proposed in this paper. The developed method derives the specific representation of constraint output by assuming that the output response is Gaussian distribution, and a variable update parameter is obtained based on the output constraint relationship at first, then the tracking filter is selectively updated with changeable update parameters and fixed update parameters, and finally, the target scale is updated with maximizing posterior probability distribution. The effectiveness of developed algorithm is verified by comparing with other trackers on OTB-50 and OTB-100 evaluation benchmark datasets, and the experimental results have shown that the suggested tracker has higher overall object tracking performance than other trackers.

scholarly journals An Improved Kernelized Correlation Filter Based Visual Tracking Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianjun Ni ◽  
Xue Zhang ◽  
Pengfei Shi ◽  
Jinxiu Zhu
Keyword(s):  
Visual Tracking ◽  
Template Matching ◽  
Visual Target ◽  
State Of The Art ◽  
Correlation Filter ◽  
Matching Method ◽  
Tracking Method ◽  
Template Update ◽  
Update Strategy ◽  
Visual Target Tracking

Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.

scholarly journals A Target Tracking Algorithm Based on Mean Shift and Fast Template Matching

2018 ◽  
Vol 36 (4) ◽  
pp. 792-799
Author(s):  
Liangdi Duan ◽  
Ping Song ◽  
Zhong Chen ◽  
Peng Zhao
Keyword(s):  
Target Tracking ◽  
Template Matching ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Current Frame ◽  
Target Template ◽  
Matching Degree ◽  
The Mean ◽  
Template Updating

This paper proposes a target tracking algorithm based on mean shift and template matching. The algorithm is divided into three stages:prediction, template matching, target positioning, and template updating. In the prediction stage, combined with the target position obtained from the previous frame tracking, the target position is predicted using the mean shift method, and the template matching search gate is defined with the predicted position as the center and the corresponding size as the coverage area. At the template matching stage, using fast template matching algorithm, the target template and search gate are quickly matched from coarse to fine, and the matching degree between matching result and target template is calculated. If the matching degree is greater than the given threshold, the fast template matching will be performed and the result will be used as the tracking result of the current frame image. Otherwise, the target position predicted by the mean shift algorithm is used as the tracking results of the current frame image. Finally, the template updating process is controlled by the tracking results of the current frame to update the target template, and the stable tracking of the target is finally completed. At the same time, the algorithm improves the robust of tracking by combining the advantages of color and edge features to the insensitivity of rotation and deformation. The method has fast calculation speed and high accuracy, it can meet real-time requirements.

scholarly journals A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2864
Author(s):  
Yuanping Zhang ◽  
Xiumei Huang ◽  
Ming Yang
Keyword(s):  
Target Tracking ◽  
Visual Tracking ◽  
Memory Function ◽  
Tracking Algorithm ◽  
Correlation Filter ◽  
Self Organization ◽  
Long Term Memory ◽  
Correlation Filters ◽  
Changing Scale

To meet the challenge of video target tracking, based on a self-organization mapping network (SOM) and correlation filter, a long-term visual tracking algorithm is proposed. Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. Filters in our method have different updating strategies and can carry out long-term tracking cooperatively. The first is the displacement filter, a kernelized correlation filter that combines contextual characteristics to precisely locate and track targets. Secondly, the scale filters are used to predict the changing scale of a target. Finally, the memory filter is used to maintain the appearance of the target in long-term memory and judge whether the target has failed to track. If the tracking fails, the incremental learning detector is used to recover the target tracking in the way of sliding window. Several experiments show that our method can effectively solve the tracking problems such as severe occlusion, target loss and scale change, and is superior to the state-of-the-art methods in the aspects of efficiency, accuracy and robustness.

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