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Fusion of Kalman filter to improve the KFMS algorithm and its application in parallel robots

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年

作者： Mei, Jiangping Su, Xiaochen Le, Yuxin Ning, Puxuan Wei, Tianlin Tianjin Univ Sch Mech Engn Tianjin 300350 Peoples R China

An improved mean shift target tracking algorithm integrated with the Kalman Filter (KFMS algorithm) is proposed to address the challenge of accurately tracking targets in complex environments using parallel robots. This algorithm combines the local search capability of the mean shift algorithm with the state estimation capability of the Kalman Filter, reducing the impact of complex environments on tracking performance. It achieves accurate global tracking of targets by the camera, effectively improving the stability and accuracy of dynamic target tracking. Experimental results with a local camera show that the target tracking accuracy and success rate of the improved KFMS algorithm are increased by 21.3% and 29.6%, respectively, compared to the mean shift algorithm, further validating the effectiveness of the enhanced algorithm.

关键词： Visual servoing mean shift algorithm Kalman filter high-speed parallel robots

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Construction of rural landscape design system based on support vector machine and mean drift algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Hanjiang Li, Jiaojiao Hefei Univ Sch Design Hefei 230601 Peoples R China Hefei Ind Sch Dept Costume & Culture & Art Hefei Peoples R China

With the theory of beautiful China, the construction and planning of rural landscapes are getting more and more attention. However, China's rural landscape design lacks innovation and practicality. Therefore, in order to avoid the homogeneity of rural landscape design, it is necessary to ensure that rural landscape designs are consistent with the sustainable development of the rural area system. The study is based on the vector machine and the mean shift algorithm in remote sensing control for the segmentation and feature extraction of architectural images, using the function weight addition and the introduction of background suppression factor, to develop a model and algorithms that are more suitable for the diversified features of the buildings. The results indicate that the research method has a maximum accuracy of 99% in extracting architectural images, and the image quality evaluation can reach 100%. In the application analysis, it was found that the economic benefit of unused land in the countryside was low, and the green conservation information of ancient villages in the landscape was 57.3%. Therefore, the model designed by the study has good integrity for architectural image feature extraction, good accuracy, high image quality, and good application value for countryside landscape design.

关键词： rural landscapes landscape design background suppression image feature extraction vector machine mean shift algorithm function weight

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algorithm of Target Tracking Based on mean shift with RBF Neural Network

Algorithm of Target Tracking Based on Mean Shift with RBF Ne...

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27th Chinese Control Conference

作者： Zhou Bin Wang Junzheng Mao Jiali Beijing Inst Technol Sch Informat Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9787900719706

The limitation of mean shift algorithm under crossing occlusion is analyzed. To solve this problem, a new tracking algorithm using mean shift with RBF neural network is proposed. According to the formal information about the object's location, the iteration start position is found with RBF neural network. And the object's real center is calculated by mean shift algorithm. Experimental results show that the proposal algorithm is stable to solve the crossing occlusion problem, and the iteration number is reduced.

关键词： mean shift algorithm Motion object tracking RBF neural network

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Unsupervised texture segmentation with one-step mean shift and boundary Markov random fields

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PATTERN RECOGNITION LETTERS 2001年第10期22卷 1073-1081页

作者： Yang, XY Liu, J Nanyang Technol Univ Sch Elect & Elect Engn Infor Syst Res Lab Nanyang 639798 Singapore

This paper presents an unsupervised texture segmentation method with the one-step mean shift algorithm and the boundary Markov random field. For Gaussian mixture models, the one-step mean shift is capable of determining the boundary points which separate neighboring Gaussian component distribution on histograms. The one-step mean shift algorithm is able to provide a coarse image segmentation result based on the image histogram. In order to improve the segmentation result with the constraints of smoothness, the boundary Markov random field is introduced. In the boundary Markov random field, the multilevel logistic distribution (MLL) is employed for the purpose of smoothing regions with its characteristic of region forming, and the boundary information is added to the energy function of the DLL distribution to preserve the discontinuity at boundaries. (C) 2001 Published by Elsevier Science B.V.

关键词： Markov random field mean shift algorithm simulated annealing texture segmentation

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Kernel Smoothing, mean shift, and Their Learning Theory with Directional Data

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JOURNAL OF MACHINE LEARNING RESEARCH 2021年第1期22卷 1-92页

作者： Zhang, Yikun Chen, Yen-Chi Univ Washington Dept Stat Seattle WA 98195 USA

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of the directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of the algorithm, we evaluate it as a mode clustering method on both simulated and real-world data sets.

关键词： Directional data mean shift algorithm kernel smoothing mode clustering optimization on a manifold

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Applying mean shift, motion information and Kalman filtering approaches to object tracking

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ISA TRANSACTIONS 2012年第3期51卷 485-497页

作者： Mazinan, Amir Hooshang Amir-Latifi, Arash IAU S Tehran Branch Tehran Iran

Contemporary research is developing techniques to tracking objects in videos using color features, and the mean shift (MS) algorithm is one of the best. This known algorithm is employed to find the location of an object, in image sequence, by using a coefficient called the Bhattacharyya coefficient. This coefficient is calculated through an object tracking algorithm to present the similarity in appearance between an object and its candidate model, where the best representation of an object is acquired, once this is could be maximized. However, the MS algorithm performance is confounded by color clutter in background, various illuminations, occlusion types and other related limitations. Because of such effects, the algorithm necessarily decreases the value of the Bhattacharyya coefficient, indicating reduced certainty in the object tracking. In the present research, an improved convex kernel function is proposed to overcome the partial occlusion. Afterwards, in order to improve the MS algorithm against the low saturation and also sudden light, changes are made from motion information of the desired sequence. By using both the color feature and the motion information simultaneously, the capability of the MS algorithm is correspondingly increased, in the present approach. Moreover, by assuming a constant speed for the object, a robust estimator, i.e., the Kalman filter, is realized to solve the full occlusion problem. At the end, experimental results on various videos verify that the proposed method has an optimum performance in real-time object tracking, while the result of the original MS algorithm may be unsatisfied. (c) 2012 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Kalman filter mean shift algorithm Bhattacharyya coefficient Occlusion Target model Candidate model

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mean shift blob tracking with kernel histogram filtering and hypothesis testing

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PATTERN RECOGNITION LETTERS 2005年第5期26卷 605-614页

作者： Peng, NS Yang, J Liu, Z Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China Henan Univ Sci & Technol Inst Elect & Informat Luoyang 471039 Peoples R China

We propose a new adaptive model update mechanism for the real-time mean shift blob tracking. Since the Kalman filter has been used mainly for smoothing the object trajectory in the tracking system, it is novel for us to use adaptive Kalman filters for filtering object kernel histogram so as to obtain the optimal estimate of object model. The acceptance of the object estimate for the next frame tracking is determined by a robust criterion, i.e. the result of hypothesis testing with the samples from the filtering residuals. Therefore, the tracker can not only update object model in time but also handle severe occlusion and dramatic appearance changes to avoid over model update. We have applied the proposed method to track real object under the changes of scale and appearance with encouraging results. (c) 2004 Elsevier B.V. All rights reserved.

关键词： mean shift algorithm object tracking adaptive kalman filter hypothesis testing model update

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Properties of mean shift

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020年第9期42卷 2273-2286页

作者： Yamasaki, Ryoya Tanaka, Toshiyuki Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan

We study properties of the mean shift (MS)-type algorithms for estimating modes of probability density functions (PDFs), via regarding these algorithms as gradient ascent on estimated PDFs with adaptive step sizes. We rigorously prove convergence of mode estimate sequences generated by the MS-type algorithms, under the assumption that an analytic kernel function is used. Moreover, our analysis on the MS function finds several new properties of mode estimate sequences and corresponding density estimate sequences, including the result that in the MS-type algorithm using a Gaussian kernel the density estimate monotonically increases between two consecutive mode estimates. This implies that, in the one-dimensional case, the mode estimate sequence monotonically converges to the stationary point nearest to an initial point without jumping over any stationary point.

关键词： Kernel Probability density function Convergence Clustering algorithms Estimation Bandwidth Trajectory Mode estimation mode clustering mean shift algorithm conditional mean shift algorithm subspace constrained mean shift algorithm

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mean shift tracking through scale and occlusion

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IET SIGNAL PROCESSING 2012年第5期6卷 534-540页

作者： Dulai, A. Stathaki, T. Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England

This study describes a method for tracking objects through scale and occlusion. The technique presented is based on the mean shift algorithm, which provides an efficient way to track objects based on their colour characteristics. A novel and efficient method is derived for tracking through changes in the target scale, where an object of interest moves away or towards the camera and therefore appears to change size in the image plane. The method works by interleaving spatial mean shift iterations with scale iterations. It is shown that this method is considerably more efficient than other methods and possesses other advantages too. It is also demonstrated that the Bhattacharyya coefficient, a histogram similarity metric that is used in the mean shift framework, can be used to reliably detect when target occlusion occurs. In such situations, the motion of an object can be extrapolated to give an accurate estimate of its position. This is used as the basis of a technique for tracking through occlusion. Experimental results are presented on data from various scenarios.

关键词： Computer vision and image processing techniques extrapolation position estimate image motion analysis colour characteristics Optical, image and video signal processing object motion occlusion spatial mean shift iteration Bhattacharyya coefficient scale iteration mean shift algorithm histogram similarity metric image colour analysis mean shift tracking Interpolation and function approximation (numerical analysis) image plane object tracking cameras camera

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Energy-Efficient Trajectory Design and Unsupervised Clustering for UAV-Aided Fair Data Collections with Dense Ground Users

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IEEE Internet of Things Journal 2025年

作者： Song, Dan Zhai, Xiangping Bryce Liu, Xin Liu, Zhiquan Tan, Chee Wei Li, Congduan Sun Yat-sen University School of Electronics and Communication Engineering Shenzhen 518107 China Huangpu Customs District P.R.China Guangdong Provincial Key Laboratory of Intelligent Port Security Inspection Guangzhou 510700 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing 211106 China Dalian University of Technology School of Information and Communication Engineering Dalian 116024 China 54th Research Institute of CECT Shijiazhuang 050081 China Jinan University College of Cyber Security Guangzhou 510632 China Nanyang Technological University Nanyang Ave Singapore Singapore

In remote or high-demand wireless cellular networks, efficient data collection from ground users (GUs) with fixed infrastructure poses a significant challenge. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their flexible deployment and cost-effectiveness. This paper focuses on a UAV-aided wireless cellular communication system comprising a UAV and multiple adjacent GUs, where the mission of the UAV is to collect data from these GUs. The objective is to minimize UAV propulsion energy consumption while ensuring fair data uploading among all GUs. Due to the non-convex and intractable nature of the above problem, we propose a novel real-time waypoint localization method based on the parallel projection method from a geometric perspective. By enhancing the projection process, this approach achieves energy-efficient and fair data collection, along with an efficient trajectory design algorithm. Further, considering the scenario of densely distributed GUs in large-scale areas, a GU-clustering algorithm is proposed based on mean shift. Additionally, this paper categorizes GUs into homogeneous and heterogeneous scenarios and designs distinct trajectory designing algorithms to accommodate diverse real-world situations. Simulations and comparisons validate the effectiveness and efficiency of the proposed algorithms in tackling the UAV trajectory design challenges. © 2014 IEEE.

关键词： mean shift algorithm projection algorithms trajectory design Unmanned aerial vehicle (UAV) unsupervised clustering

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