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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Zhe, Shengxiang Yunnan Univ Business Management Student Ctr Kunming 650106 Yunnan Peoples R China

ISBN: (纸本)9781665423144

With the rapid development of Internet technology and network communication technology, college physical education is transforming from the traditional manual operation management to the centered college teaching management model. This paper integrates the kcf algorithm, through the online and offline teaching mode, aims to improve the interest of college students in learning sports and the main role of students in teaching, And in the teaching reform to meet the requirements of quality education, for the university sports teaching reform innovation new mode.

关键词： kcf algorithm College physical education reform Empirical research

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Research on Target Loss Early Warning of kcf algorithm Based on Hypothesis Test

Research on Target Loss Early Warning of KCF Algorithm Based...

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Conference on Optical Sensing and Imaging Technologies and Applications / International Symposium on Optoelectronic Technology and Application (OTA) / Annual Conference of the Chinese-Society-for-Optical-Engineering (CSOE)

作者： Zhang Ying Xu Chuan-gang Zhang Feng Song Li-quan Tianjin Jinhang Inst Tech Phys Tianjin 300308 Peoples R China

ISBN: (纸本)9781510623354

The kcf algorithm uses the response values of the template and the test sample to calculate the target position. The location of the maximum response value is the target region. However, the kcf algorithm does not set the target loss warning mechanism. The target will be lost when the tracking process encounters complicated conditions such as scale chance, rapid movement, and severe obstruction. At this point, the tracker will update the background information into the template. The accumulated deviation of the template will cause the tracker cannot be correctly identified and tracked when it encounters the target again. To solve this problem, through the statistics of the maximum response value of each frame of the video sequence, it founds that the overall response of the video sequence has a normal distribution trend, and the maximum response value of the frame will fluctuate abnormally in the frames that are lost or are about to be lost. This paper uses the idea of hypothesis testing in mathematical statistics. This paper uses a set of fixed-dimensional response peak data to perform hypothesis testing on the peak value of the current frame response. If the response peak of the current frame falls within the rejection region, the target is determined to be lost or is about to be lost. The experimental results prove that the proposed method can correctly implement the early warning function when verifying the OTB standard test sequence. It can provide reference for when to load redetection after target is lost.

关键词： hypothesis testing kcf algorithm missing target

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Study of Dynamic Tracking algorithms for Apples Under the Influence of Oscillation

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IEEE ACCESS 2020年 8卷 32966-32974页

作者： Jiang, Mei Wu, Qian Sun, Sashuang Shang, Yuying Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

Influenced by the force of the wind and agricultural operations, fruits often undergo oscillation, which makes it difficult to automatically monitor their growing status. It is very important to realize dynamic tracking of these oscillating fruits in order to improve automatic monitoring systems and the efficiency of picking robots. In order to investigate the accuracy of the tracking of oscillating fruits, three classic tracking algorithms were adopted and compared: the kernelized correlation filter algorithm (kcf), the compressive tracking algorithm (CT), and the multi-task tracking algorithm (MTT). The effectiveness of these algorithms was verified by testing six video sequences acquired in different environments, and three indices (the average central error, frame loss rate, and time efficiency) were used to verify their performance. The results showed that the kcf algorithm was most appropriate for the tracking of oscillating fruit objects, as it has a lower centering error and a much higher frame rate.

关键词： Target tracking Monitoring Classification algorithms Heuristic algorithms Oscillators Robots Object tracking oscillation kcf algorithm CT algorithm MTT algorithm

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Automatic tracking of multi-fruit targets with oscillation under natural growth conditions

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Information Processing in Agriculture 2020年第4期7卷 546-555页

作者： Lin-lin Zhang Dong-jian He Huai-bo Song College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling 712100China

It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing *** issue of how to track green and oscillating fruits under the influence of wind and farming operations is a key aspect of monitoring of the growth state of the *** order to realize the accurate tracking of green fruit targets,a new method based on target tracking is ***,an optical flow method is applied to realize the automatic detection of green fruit targets,and this lays the foundation for the accurate and automatic tracking of these ***,Kalman and kernelized correlation filter(kcf)algorithms are applied to achieve multi-target tracking and *** order to verify the performance of these different algorithms on various types of green fruit targets,experiments were carried out based on nine video *** experimental results for the tracking of single,double and triple green fruit targets show that the average tracking success rates of the Kalman algorithm are 88.15%,82.30%and 53.10%,respectively,and those of the kcf algorithm are 94.07%,87.35%and 61.46%,respectively,meaning that the average tracking results from kcf are 5.92%,5.05%and 8.36%higher than those from the Kalman *** time consumed is also reduced by 35.40%,36.27%and 40.86%,*** results show that it is feasible to apply the kcf algorithm to the tracking of green fruit targets.

关键词： Video tracking technology Green multi-fruit target Oscillating green fruits Kalman algorithm kcf algorithm

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Research on 3D Space Target Following Method of Mobile Robot Based on Binocular Vision 11th

Research on 3D Space Target Following Method of Mobile Robot...

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11th International Conference on Modelling, Identification and Control (ICMIC)

作者： Zhao, Xitong Cheng, Lei Peng, Rui Li, Chan Nong, Xiaoqi Wu, Huaiyu Xiong, Ling Chen, Yang Wuhan Univ Sci & Technol Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China

ISBN: (纸本)9789811504747;9789811504730

In order to solve the problem of the target following in 3D (three-dimensional) space, the kcf and SGBM fusion algorithm is proposed. In this method, the position information of the target in the camera image is obtained by kcf algorithm, the depth information of the target is calculated by SGBM algorithm, and the three-dimensional coordinate of the target in camera coordinate system is determined. The binocular vision system is set on the mobile robot, and the mobile robot achieves target following through velocity information and angular velocity information. Experiments in different indoor environments show that the algorithm has low hardware requirements, good real-time performance, and high precision. It is suitable for target tracking by a robot in three-dimensional space.

关键词： Binocular vision kcf algorithm SGBM algorithm Three-dimensional space Target follow

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Real-Time target tracking and locating system for UAV 9

Real-Time target tracking and locating system for UAV

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9th International Conference on Digital Image Processing (ICDIP)

作者： Zhang, Chao Tang, Linbo Fu, Huiquan Li, Maowen Beijing Inst Technol Sch Informat & Elect Inst Radar Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China

ISBN: (纸本)9781510613058;9781510613041

In order to achieve real-time target tracking and locating for UAV, a reliable processing system is built on the embedded platform. Firstly, the video image is acquired in real time by the photovoltaic system on the UAV. When the target information is known, kcf tracking algorithm is adopted to track the target. Then, the servo is controlled to rotate with the target, when the target is in the center of the image, the laser ranging module is opened to obtain the distance between the UAV and the target. Finally, to combine with UAV flight parameters obtained by BeiDou navigation system, through the target location algorithm to calculate the geodetic coordinates of the target. The results show that the system is stable for real-time tracking of targets and positioning.

关键词： UAV kcf algorithm target location algorithm BeiDou navigation system

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Anti-occlusion and Scale Adaptive Target Tracking algorithm Based on Kernel Correlation Filter 20

Anti-occlusion and Scale Adaptive Target Tracking Algorithm ...

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20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Wang, Chuanyun Shi, Zhongrui Si, Keyi Su, Yang Li, Zhaokui Wang, Ershen Shenyang Aerosp Univ Coll Artificial Intelligence Shenyang Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China Shenyang Aerosp Univ Sch Elect & Informat Engn Shenyang Peoples R China

ISBN: (纸本)9781665416580

The Kernel Correlation Filter Tracking algorithm(kcf)is a lightweight tracking algorithm with the advantages of fast tracking speed and good effect. However, when the target is occluded and the scale changes, the algorithm will have tracking drift and tracking loss. Aiming at the kernel-related filter tracking algorithm that cannot solve the tracking failure caused by occlusion, target scale changes and other factors in the tracking process, an anti-occlusion and scale-adaptive kernel-related filtering algorithm is proposed. We build a scale pool and use the scale pool to train a one-dimensional fast scale filter to solve the problem of target scale changes. This paper uses the average occlusion distance metric and the size of the context occlusion factor to determine the occlusion state of the target, and dynamically select the learning update rate of the target model according to the target occlusion state. When it is judged that the target is severely occluded, the target position is predicted according to the previous motion state of the target, and the small-range re-detection positioning mechanism proposed in this paper is used to re-detect the target within a certain range of the predicted position. At the same time, the re-detected target is occluded again Judgment to determine whether the target is out of the occlusion. If it is determined that the target is still severely occluded, it means that the target is not out of the occlusion area, the re-detection of the target position is inaccurate, and the predicted position is output. Experimental results show that the accuracy and success rate of the algorithm in this paper are 0.819 and 0.669, which are 8.33% and 7.04% higher than the kcf algorithm. The tracking effect of this algorithm is better than that of kcf algorithm.

关键词： Occlusion discrimination Scale adaptation kcf algorithm Location prediction

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Kernel Correlation Filter algorithm and Its Research in Small Target and Occlusion Fields

Kernel Correlation Filter Algorithm and Its Research in Smal...

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2024 International Conference on Intelligent Robotics and Automatic Control

作者： Li, Wenhu Tang, Yanfei Xiong, Youling Wang, Fei Anhui Postal & Telecommun Vocat & Tech Coll Comp & Network Coll Hefei Anhui Peoples R China Anhui Vocat & Tech Coll Commerce Sch Financial Technol Wuhu Anhui Peoples R China

ISBN: (纸本)9798350389814;9798350389807

This paper focuses on the improvement of kernel filtering algorithms and their applications, particularly on enhancing the accuracy of target recognition and tracking in modern intelligent environmental monitoring. The Kernel Correlation Filter (kcf) algorithm, due to its efficiency, high precision, and strong robustness, holds significant potential for application in surveillance environments. However, the performance of the kcf algorithm is limited in complex scenarios such as occlusion, scale changes, similar targets, and rapid movements. To address these issues, this paper proposes two improved kcf algorithms. Firstly, a scale pool algorithm is introduced, which enhances the algorithm's adaptability to target scale changes by performing multi-scale feature extraction and matching, thereby improving tracking accuracy, efficiency, and speed. Experiments demonstrate that the tracking accuracy of the improved algorithm is significantly enhanced on the OTB50 and OTB100 datasets. In experiments using a self-constructed dataset, the success rates for feature extraction, scale changes, and sampling rates were improved by 40%, 13.6%, and 29.76%, respectively. Secondly, to address the occlusion problem, an improved joint tracking algorithm combining the modified kcf with ByteTrack is proposed. The high-quality detection results of the improved kcf are utilized as input for ByteTrack, which optimizes the handling of occluded objects through its multiple matching strategies. The joint algorithm exhibits excellent performance on the OTB50 and OTB100 datasets. In the self-constructed dataset, the success rate for handling occlusion scenarios is improved by 50% compared to the original kcf, effectively solving issues such as target template updating, feature changes, and target disappearance.

关键词： Small Target Tracking Occlusion Tracking kcf algorithm Scale Pool algorithm ByteTrack algorithm

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Design of airborne target tracking accelerator based on kcf

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8966-8971页

作者： Yang, Haoran Yu, Jinxiang Wang, Shaojun Peng, Xiyuan Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China

The on-board target tracking of unmanned aerial vehicle (UAV) relies on on-board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real-time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (kcfs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real-time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the kcf algorithm. The kcf algorithm is implemented in hardware using the high-level synthesis technology, and a high-speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960x540 resolution, which meets the real-time requirements of UAV target tracking task.

关键词： autonomous aerial vehicles target tracking remotely operated vehicles field programmable gate arrays filtering theory tracking task processing ZYNQ platform hardware acceleration kcf algorithm tracking rate UAV target tracking task airborne target tracking accelerator on-board target tracking unmanned aerial vehicle on-board computer collected ground image information target tracking algorithm target tracking function target position power consumption frame rate UAV's image collection system traditional target tracking platforms tracking platform study designs airborne ground target tracking accelerator correlation filters algorithm low computational complexity

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Research on target recognition and path planning for EOD robot

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2018年第4期57卷 325-333页

作者： Deng, Wei Zhang, Hui Li, Yibin Gao, Feng Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Qilu Univ Technol Sch Elect Engn & Automat Jinan 250353 Shandong Peoples R China

In order to improve the autonomy of the explosive ordnance disposal (EOD) robot, the paper makes a research on the target recognition and path planning algorithm based on laser range finder and Time of Flight (TOF) camera. The target position of the explosive is selected by the operator from the grey-scale image, then EOD robots use Kernelised Correlation Filters (kcf) tracking algorithm and distance constraint to realise the real-time explosives identification. In the case of A* algorithm, it does not take the heading information of the robot into consideration, which may lead to the problem of heading shakes in the vicinity of the obstacle. In this paper, the heading constraint is introduced to improve the A* algorithm so as to eliminate the problem of heading shakes and improve the stability of the robot. The simulation and experiment results demonstrate the feasibility of the algorithm proposed in the paper.

关键词： EOD robot kcf algorithm path planning TOF camera

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