检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Coupled-layer based visual tracking via adaptive kernelized correlation filters

引用

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

来源：评论

学校读者我要写书评

暂无评论

Object Tracking with Convolutional Neural Networks and kernelized correlation filters 29

Object Tracking with Convolutional Neural Networks and Kerne...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Improved kernelized correlation filters Tracking Algorithm with Adaptive Learning Factor 35

Improved Kernelized Correlation Filters Tracking Algorithm w...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Visual tracking for underwater sea cucumber via correlation filters

引用

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

来源：评论

学校读者我要写书评

暂无评论

Improved Kernel correlation Filter Based Moving Target Tracking for Robot Grasping

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Peng, Fang Xu, Qinyi Li, Yifei Zheng, Maoxi Su, Hang Univ Elect Sci & Technol China Zhongshan Inst Sch Mech & Elect Engn Zhongshan 528402 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

Tracking and grasping moving objects are hot topics in the field of robots, which provides great potential in the industrial scene and human-computer cooperation. Based on the kernel correlation filter and vision 3-D reconstruction, this article proposes a visual-based tracking and grasping method for moving targets. An improved algorithm based on the kernel correlation filter is proposed for object tracking. A scale pool is constructed, and a scale filter is trained to solve the problem of algorithm scale adaptation. At the same time, the judgment mechanism of tracking results, secondary detection, and modification update mechanisms are added to improve the robustness of the algorithm. Combined with an RGB-D camera, the target is reconstructed to obtain the 3-D pose of the target. The tracking and intercepting strategy is adopted to grasp the moving target. The proposed method is proven to have good performance through dataset comparison tests and experiments on real robot systems.

关键词： Target tracking Filtering algorithms Grasping Robots Kernel correlation Training 3-D reconstruction kernelized correlation filters scale adaptation secondary detection tracking and grasping

来源：评论

学校读者我要写书评

暂无评论

A Visual Object Tracking Algorithm Based on Improved TLD

引用

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

来源：评论

学校读者我要写书评

暂无评论

Robust visual tracking via identifying multi-scale patches

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Fast Teacher Pose Estimation Framework Base on kernelized correlation Filter and Spatial Transformed High-Resolution Network

引用

Procedia Computer Science 2020年 174卷 393-399页

作者： Tianyou Xu Junqi Guo Muzhu Hong Ludi Bai School of Artificial Intelligence Beijing Normal University Beijing 100875 China

Smart Education gradually becomes more of a concern for people, it providing sharable education resources, diversified teaching methods and data-based teaching evaluation methods. Virtual teacher is a part of smart education and it helps on improving learners’ cognitive level. The difficulty of virtual teacher technology lies in accurately recording and reproducing the actions and postures of teachers. It requires an accurate and fast teacher pose estimation algorithm as a support. This paper proposes a teacher pose estimation framework based on KCF and ST-HRN. Firstly, the target detection network YOLOv3 and the similarity algorithm based on SIFT feature are used to predict the position of the teacher in the video, and the KCF target tracking algorithm is used to accelerate the calculation. Secondly, based on the high-resolution network, the ST-HRN single-person pose estimation model is proposed and trained, and the average [email protected] =90.2% accuracy is obtained on the MPII dataset. Finally, the framework is tested on class teaching videos and have 30.8% estimate speed promotion by implementing tracking algorithm.

关键词： Teacher Pose Estimation kernelized correlation filters Spatial Transformed High-Resolution Network Smart Education

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：