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17th International Conference on Intelligent Robotics and Applications

作者： Dong, Yongsheng Zhou, Haotian Zhou, Junxian Min, Huasong Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Wuhan 430081 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

Vision-based seam tracking is a crucial technology widely used in robotic welding. However, during the welding process, intense noise from the arc and spatter leads to large welding seam tracking errors. To solve this problem, this paper proposes a seam tracking method that integrates a feature-fused kernelized correlation filters (FF-KCF) with a Generative Adversarial Network. Firstly, FF-KCF fuses histogram of oriented gradients (HOG) and scale invariant local ternary pattern (SILTP) features to overcome the limitations of individual features and capture a comprehensive feature representation of the weld seam. Secondly, a feature-supervised Conditional Generative Adversarial network is introduced to repair laser stripe images affected by intense noise, addressing model drift issues in noisy environments and long sequence tracking. Model parameter updates and image repair frequencies are controlled by the Peak-to-Sidelobe Ratio (PSR) to enhance tracking efficiency. Finally, a direction clustering least squares fitting algorithm (DC-LS) is proposed to identify feature points in the initial frame image, solving the problem of manually specifying the tracking object in the initial frame required by the FF-KCF. The proposed methodology is validated on the collected dataset. The experimental results show that the tracking speed can reach up to 17.3 FPS, and the average error is kept within 2.2 pixels. Compared to state-of-the-art methods, the proposed method demonstrates superior performance in terms of accuracy and stability, meeting the requirements for precision and real-time seam tracking.

关键词： Robotic welding Laser vision sensor Seam tracking kernelized correlation filters Conditional generation adversarial network

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Coupled-layer based visual tracking via adaptive kernelized correlation filters

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VISUAL COMPUTER 2018年第1期34卷 41-54页

作者： Zhang, Haoyang Liu, Guixi Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China

Part-based visual model is particularly useful when the target appearance undergoes partial occlusion or deformation. The existing reliable patches tracking (RPT) method has achieved better result by identifying and exploiting the reliable patches that can be tracked correctly, yet it tends to fail in some challenging scenes since it ignores the holistic information of target completely, while, in fact, the target's holistic appearance provides more discriminative features than local patches with low resolution. Based on the existing RPT and kernelized correlation filters tracking method, in this paper, we propose a coupled-layer visual model based tracker by combining the target's global and local appearance in a coupled way. The global layer provides the holistic information and is treated as an approximation of the target. The local layer is composed of multiple small patches that are randomly initialized in the first frame. During tracking, the global tracker detects the target itself;its detection result is employed in the local layer to exploit the reliable patches and to estimate the target position corresponding to each patch. The exploited reliable patches are employed to estimate the target scale and to vote the current target location. Finally, both global and local models are updated with carefully designed updating mechanisms. Experiments conducted on 80 challenging benchmark sequences clearly show that our tracker improves the RPT tracker significantly both in overall and individual performance yet without obvious speed cost. Also, our tracker outperforms all the state-of-the-art trackers in overall datasets and eight independent datasets.

关键词： Coupled-layer visual model Reliable patches kernelized correlation filters Visual tracking

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Visual tracking tracker via object proposals and co-trained kernelized correlation filters

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VISUAL COMPUTER 2020年第6期36卷 1173-1187页

作者： Mbelwa, Jimmy T. Zhao, Qingjie Wang, Fasheng Univ Dar Es Salaam Dept Comp Sci & Engn POB 33335 Dar Es Salaam Tanzania Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Technol Beijing 100081 Peoples R China Dalian Minzu Univ Sch Informat & Commun Engn Dalian 116600 Peoples R China

Visual tracking is a challenging task in the field of computer vision with wide applications in intelligent and surveillance systems. Recently, correlation trackers have shown great achievement in visual tracking due to its high efficiency. However, such trackers have a problem of handling fast motion, motion blur, illumination variations, background clutter and drifting away caused by occlusion and thus may result in tracking failure. To solve this problem, we propose a tracker that is based on the object proposals and co-kernelized correlation filters (Co-KCF). The proposed tracker utilizes both object proposals and global prediction estimated by kernelized correlation filter scheme to obtain best proposals as prior information using spatial weight strategy in order to improve tracking performance of fast motion and motion blur. Since single kernel may lead to background clutter and drifting problem, Co-KCF has been employed to combat this defect and predict a new state of a target object. Extensive experiments demonstrate that our proposed tracker outperforms other existing state-of-the-art trackers.

关键词： kernelized correlation filters correlation filter Visual tracking Object proposals

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Improved kernelized correlation filters Tracking Algorithm with Adaptive Learning Factor 35

Improved Kernelized Correlation Filters Tracking Algorithm w...

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第35届中国控制会议

作者： PEI Mengxin LI Weixing KE Zunjie GAO Qi School of Automation Beijing Institute of Technology

ISBN: (纸本)9781509009107

Tracking with kernelized correlation filters is a new idea recently proposed which is different from traditional methods based on target *** method achieves fast tracking speed,however,it is seriously compromised when the tracking target has large-scale changes and severe *** improved update model based on kernelized correlation filters is proposed in this paper to effectively overcome the above *** adaptive learning factor is defined with Peak-to-sidelobe ratio which estimates the correlation between different candidate *** achieves adaptive online update of the tracking *** demonstrates that the presented algorithm can adjust the learning factor in real time according to different scenarios,which results increased success rate of *** the adaptive learning factor,the presented algorithm shows advanced adaptability to partial occlusions,illumination,and target scale variations.

关键词： target tracking kernelized correlation filters adaptive learning factor

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Object Tracking with Convolutional Neural Networks and kernelized correlation filters 29

Object Tracking with Convolutional Neural Networks and Kerne...

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第29届中国控制与决策会议

作者： Dongxuan Li Wenjie Chen School of Automation Beijing Institute of Technology Beijing Key Laboratory of Automatic Control System (Beijing Institute of Technology)

ISBN: (纸本)9781509046584

Convolutional neural networks are widely used in object recognition and *** recent years,some researchers attempt to apply deep neural networks to visual object ***,deep networks are extremely time-consuming and object tracking is not a classification problem *** this paper,we present an online tracking framework which combines shallow convolutional neural networks with kernelized correlation filters(KCF).Different from offline training,our method successfully gets the convolution kernels by K-means clustering *** results based on a representative visual tracker benchmark dataset show that the proposed method achieves excellent performance.

关键词： Object Tracking Convolutional Neural Networks kernelized correlation filters

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Robust visual tracking via identifying multi-scale patches

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第11期78卷 14195-14230页

作者： Liang, Yun Li, Ke Zhang, Jian Wang, Meihua Lin, Chen South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Zhengzhou Sch Surveying & Mapping Zhengzhou 450052 Henan Peoples R China Zhejiang Int Studies Univ Sch Sci & Technol Hangzhou 310012 Zhejiang Peoples R China Xiamen Univ Dept Comp Sci Xiamen 361005 Fujian Peoples R China

The complex changes of target and its surroundings introduce several tracking challenges, such as occlusion, deformation and so on. Many challenges coexist in a video which makes tracking still under successfully solved. The present trackers deal with coexisting challenges in a common model for all components of target. However, different components often undergo different challenges at the same time, while some with deformation and others with occlusion. The common model cannot adapt to these challenges simultaneously. An effective method is to separately deal with the challenges. This paper proposes a new robust tracker via separately tracking and identifying the multi-scale patches of target to cope with the coexisting challenges. It is achieved by three respects. Firstly, we define a new basic tracker by introducing the gaussian mixture model into kernelized correlation filters (KCF). For the KCF is very sensitive to the similar surroundings, we construct a regular term and a loss function via the gaussian mixture model to optimize the classifier formed by KCF. Secondly, we define a new appearance representation model of target by multi-scale patches. To deal with the different variations of patches, we separately construct and update their appearance representations. Thirdly, with the tracked result of each patch computed by our basic tracker, we use the structure information and the Hough Vote to decide the target. Then, our method improves the accuracy by rejecting the failed tracked patches. Many experiments have been achieved on the Tracking Benchmark, and the quantitative and qualitative evaluations show that the proposed tracker performs better than most of the present trackers.

关键词： Multi-scale patches Visual tracking kernelized correlation filters Gaussian mixture model Hough vote

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A Visual Object Tracking Algorithm Based on Improved TLD

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ALGORITHMS 2020年第1期13卷 15页

作者： Zhen, Xinxin Fei, Shumin Wang, Yinmin Du, Wei Southeast Univ Sch Automat Nanjing 210096 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Visual object tracking is an important research topic in the field of computer vision. Tracking-learning-detection (TLD) decomposes the tracking problem into three modules-tracking, learning, and detection-which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments were conducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes. Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.

关键词： object tracking tracking-learning-detection kernelized correlation filters histogram of oriented gradient real time robustness

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Visual tracking for underwater sea cucumber via correlation filters

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International Journal of Agricultural and Biological Engineering 2023年第3期16卷 247-253页

作者： Honglei Wei Xiangzhi Kong Xianyi Zhai Qiang Tong Guibing Pang School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian 116038LiaoningChina

One of the essential techniques for using underwater robots to fish sea cucumbers is that the robots must track sea cucumbers using computer vision *** underwater targets is a challenging task due to suspension,water absorption,and light *** study proposed a simple but effective algorithm for sea cucumber tracking based on kernelized correlation filters(KCF)*** method tracked the head and tail of the sea cucumber respectively and calculated the scale change according to the distance between the head and *** KCF method was improved on three *** of all,the target was searched at the predicted position to improve ***,an adaptive learning rate updating method based on the detection score of each frame was ***,the adaptive size of the histogram of the oriented gradient(HOG)feature was used to balance the accuracy and *** results showed that the algorithm had good tracking performance.

关键词： visual tracking correlation filters kernelized correlation filters sea cucumber scale estimation underwater

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Object Tracking Based on Mean Shift Algorithm and kernelized correlation Filter Algorithm 24th

Object Tracking Based on Mean Shift Algorithm and Kernelized...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Zhou, Huazheng Ma, Xiaohu Bian, Lina Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Peoples R China

ISBN: (纸本)9783319700908;9783319700892

In order to solve the problems of motion blur and fast motion, a new robust object tracking algorithm using the kernelized correlation filters (KCF) and the Mean Shift (MS) algorithm, called KCFMS is presented in this paper. The object tracking process can be described as: First, we give the initial position and size of the object and use the Mean Shift algorithm to obtain the position of the object. Second, the kernelized correlation Filtering algorithm is used to obtain the position of the object in the same frame. Third, we use the cross update strategy to update the object models. In order to improve the tracking speed as much as possible, our object tracking algorithm works only over one layer. This hybrid algorithm has a good tracking effect on the target fast motion and motion blur. We present extensive experimental results on a number of challenging sequences in terms of efficiency, accuracy and robustness.

关键词： kernelized correlation filters Mean shift Motion blur Fast motion

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An adaptive KCF tracking via multi-feature fusion 7

An adaptive KCF tracking via multi-feature fusion

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7th International Conference on Virtual Reality and Visualization (ICVRV)

作者： Guo De-quan Peng Sheng Ling Sheng-gui Yang Hong-yu Liu Hong Chengdu Univ Informat Technol Chengdu 610225 Sichuan Peoples R China CAAC Southwest Reg Adm Chengdu 610200 Sichuan Peoples R China Sichuan Univ Key Lab Fundamental Synthet Vis Graph & Image Sci Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9781538626368

To tackle the problem of target scale changed, too monotonous target feature, or track cumulative errors in kernelized correlation filters(KCF), the paper proposes a self-adaptive KCF tracking algorithm employed multi-feature fusion. KCF tracking algorithm is improved based on location prediction, multi-feature fusion and bilinear interpolation. Among them, to facilitate better representation of the target's appearance model, make target tracking more robust, the multi-feature fusion is integrated (Hue Saturation Value, HSV) color features, grayscale features and improved (Histogram of Oriented Gradient, HOG) features. Both qualitative and quantitative evaluations on some object tracking benchmark show that the proposed tracking method achieves superior performance compared with other state-of-the-art methods.

关键词： Object tracking kernelized correlation filters Multi-feature fusion Scale self-adaption

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