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Automatic motion recognition technology based on fuzzy clustering algorithm and VR video image

ALEXANDRIA ENGINEERING JOURNAL 2025年 124卷 462-469页

作者： Xu, Ganbin Lin, Jianwei Zhejiang Police Coll Police Command & Tact Dept Hangzhou 310000 Peoples R China Zhejiang Shuren Univ Coll Informat Sci & Technol Hangzhou 310015 Peoples R China

The rapid development of Virtual Reality (VR) technology has heightened interest in immersive video applications. Current dynamic video segmentation methods emphasize iterative pixel-level classification based on fuzzy membership levels. Traditional motion recognition, however, struggles with posture variations in complex scenarios, leading to inaccuracies. To address this, we propose a fuzzy clustering-based framework for VR video motion recognition, enhancing accuracy through optimized pixel segmentation and preprocessing. The system's stability across varying frame rates was evaluated. Experiments utilized a CCD-captured database of six motion poses (sitting, squatting, kneeling, etc.). Results demonstrated 96% accuracy for squat detection, 6.7% false positives for kneeling postures, and a 2%-8% error range for sitting poses-significantly higher than other categories due to postural variability. This study advances VR motion recognition by integrating fuzzy clustering with adaptive image processing, offering robust solutions for immersive training, rehabilitation monitoring, and human-computer interaction systems.

关键词： Virtual reality Video images fuzzy clustering algorithm Automatic recognition Image segmentation

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PMWFCM: A Possibility based MultiKernel Weighted fuzzy clustering algorithm for classification of driving behaviors

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ALEXANDRIA ENGINEERING JOURNAL 2025年 113卷 249-261页

作者： Ma, Ning Wu, Kaijun Yuan, Yubin Li, Jiawei Wu, Xiaoqiang Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China Ordos Inst Technol Sch Mech & Traff Engn Ordos 017000 Peoples R China

fuzzy clustering algorithms are widely applied in the field of traffic driving, aiding in the classification of driving behaviors from massive traffic data and enhancing traffic safety levels. However, classical fuzzy CMeans (FCM) algorithms are sensitive to noise during the clustering process, leading to suboptimal performance when dealing with traffic datasets with lower accuracy. Moreover, single-kernel clustering algorithms are greatly influenced by kernel function selection. To address these issues, this paper proposes a Possibility Weighted Multi-Kernel fuzzy clustering algorithm (PMWFCM). By integrating possibility-based fuzzy clustering with FCM and introducing a multi-kernel weighting mechanism, PMWFCM effectively reduces FCM's sensitivity to outliers while resolving issues of clustering consistency in Possibilistic C-Means (PCM) algorithms, overcoming the challenges associated with kernel function selection. Validation on three different types of datasets demonstrates that the PMWFCM algorithm performs exceptionally well in terms of average accuracy, normalized information, average time, robustness, and convergence. When applied to the evaluation of driving behaviors in traffic datasets. Therefore, the improved FCM algorithm proposed in this paper can accurately and comprehensively reflect changes in traffic data, providing a solid theoretical foundation for identifying and assessing major risk types among passenger drivers.

关键词： fuzzy clustering algorithm Possibilistic fuzzy clustering Kernel function Traffic driving assessment

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fuzzy clustering algorithm for time series based on adaptive incremental learning

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第4期38卷 3991-3998页

作者： Wang, Wei Hu, Xiaohui Wang, Mingye Beihang Univ Coll Automat Sci & Elect Engn Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China

With the development of Internet technology, the growth of network services is accelerating. For more and more network service requests, how to ensure the response speed and query accuracy required by users is a huge challenge. In order to realize fast clustering of large data business request data and improve the accuracy of clustering. This paper presents a data fuzzy clustering algorithm based on Adaptive Incremental learning time series. The algorithm defines large data clustering in time series, and the incremental time series clustering method is used. Firstly, the complexity of network data is reduced by data compression, and then time series data clustering based on service time similarity is carried out. In this paper, the time series fuzzy clustering algorithm based on Adaptive Incremental Learning inherits the clustering structure information obtained by previous clustering. Initialize the current clustering process, and then search the outlier samples in the current data block adaptively without setting parameters. Automatically create new clusters from outlier samples, and finally check empty cluster recognition. Identification determines whether certain clusters need to be deleted to ensure the efficiency of subsequent cluster processes. The experimental results show that the algorithm has good clustering accuracy and efficiency for isochronous and unequal time series.

关键词： Network data adaptive incremental learning time series fuzzy clustering algorithm

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fuzzy clustering algorithm-Based Classification of Daily Electrical Load Patterns 12

Fuzzy Clustering Algorithm-Based Classification of Daily Ele...

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12th International Conference on fuzzy Systems and Knowledge Discovery (FSKD)

作者： Sun, Yi Gu, Wei Lu, Jun Yang, Zenghui North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China State Grid Shanghai Elect Power Res Inst Shanghai Peoples R China

ISBN: (纸本)9781467376822

As the development of the smart grid data acquisition system, many smart meters are loaded in the smart grid to acquire the power users' real-time load data. These real-time data are very important. Because these data contain users electrical behavior features. However the smart grid has too many users and data, it is in-feasible to process each user's data and analyze each user's behavior. To solve this problem, this paper uses the fuzzy clustering algorithm to classify smart grid users before analyzing users' power consumption behavior. And this paper calculates two cluster validity indexes to determine the optimal number of clusters. At last, the simulation result shows that the fuzzy clustering algorithm can play an important role for solving the smart grid users' clustering question.

关键词： fuzzy clustering algorithm smart grid power users clustering cluster validity index

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A fuzzy clustering algorithm-Based Dynamic Equivalent Modeling Method for Wind Farm With DFIG

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2015年第4期30卷 1329-1337页

作者： Zou, Jianxiao Peng, Chao Xu, Hongbing Yan, Yan Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

With the increasing capacity of grid connected wind farms, the influence of wind power to stable operation of an electric power system is becoming more and more important. In order to analyze the active power output characteristics of wind farm, a multimachine representation dynamic equivalent method based on the fuzzy clustering algorithm is proposed. First, indicators which can characterize the active power output performance of a doubly fed induction wind generator (DFIG) are researched. Second, a fuzzy C-means (FCM) clustering algorithm is first applied to the modeling of wind farm. DFIGs are divided into groups by analyzing the indicator data with FCM. Finally, DFIGs of the same group are equivalent as one DFIG to realize the dynamic equivalent modeling of wind farm with DFIG. Simulation results demonstrated that the established dynamic equivalent model can reflect the active power dynamic response characteristics of wind farm with DFIG effectively;meanwhile, the model of wind farm is simplified and computation complexity is reduced.

关键词： Active power characteristic analysis dynamic equivalent model fuzzy clustering algorithm multimachine representation method wind farm with double fed induction wind generator (DFIG)

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A modified strategy of fuzzy clustering algorithm for image segmentation

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SOFT COMPUTING 2015年第11期19卷 3261-3272页

作者： Zhou, Dongguo Zhou, Hong Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China

fuzzy clustering algorithm is a frequently used method for image segmentation, which allows pixel to be classified into one or more clusters with respect to its membership level. However, its segmentation performance often suffered from the factors associated with the drift of cluster centers and the sensitiveness to the intensity overlap of distribution between classes. In this paper, we solve these drawbacks and present a modified strategy of fuzzy clustering algorithm for image segmentation. This strategy generally consists of two-pass processes. The first process is to directly calculate the cluster centers from the segmented image and then take the higher value of cluster centers as an alternative threshold to prevent the pixels with lower intensity from clustering. The second process thereby makes use of the fuzzy clustering algorithm with a bias field for partitioning pixels with spatial proximity, ensuring that our method is less sensitive to the drawbacks inherent in the fuzzy clustering algorithm and thus obtaining promising results. Experiments on synthetic and some representative infrared images demonstrate that the proposed method outperforms fuzzy c-means methods and its existing variants in terms of segmentation performance, and is less sensitive to the intensity overlap of the distribution between classes.

关键词： Image segmentation Infrared image fuzzy clustering algorithm Cluster center

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Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation

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APPLIED SOFT COMPUTING 2018年 68卷 586-596页

作者： Mahata, Nabanita Kahali, Sayan Adhikari, Sudip Kumar Sing, Jamuna Kanta Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Cooch Behar Govt Engn Coll Ghughumari Cooch Behar India

This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Brain MR image segmentation fuzzy clustering algorithm Intensity inhomogeneity Bias field

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A method for extracting gray level image feature based on fuzzy clustering algorithm

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2018年第4期35卷 4385-4397页

作者： Zhou, Gaiyun Ma, Li Li, Zhanguo Zhang, Guoping Kim, C. Pingdingshan Univ Coll Comp Pingdingshan 467000 Peoples R China Univ Southern Calif Dept Econ Los Angeles CA USA

Currently, the method was not applicable to the requirement of feature extraction of different types of grayscale images, resulting in the feature extraction results with low accuracy, long time consumption, low clarity and poor flexibility. In this article, a method of extracting feature of gray image based on fuzzy clustering algorithm was proposed. The grayscale, the median filtering, the edge detection and mathematical morphology processing were carried out for the color image of CCD camera collected by acquisition card. Then, sample feature object of target object gray level image and object of target feature were obtained. The similarity between sample feature object of target object gray level image and object of target feature was obtained through calculation. Moreover, the feature conforming to the set threshold was selected. Meanwhile, the grayscale image feature extraction results with different requirements were obtained through adjusting gray level image matrix and similarity parameters. From comparison and analysis of experimental result, we can see that the correctness, effectiveness and flexibility of proposed method are proved for different types of gray level image feature extraction. The extraction result has high definition and short running time.

关键词： fuzzy clustering algorithm gray level image feature extraction similarity

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A Selection Model for Optimal fuzzy clustering algorithm and Number of Clusters Based on Competitive Comprehensive fuzzy Evaluation

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IEEE TRANSACTIONS ON fuzzy SYSTEMS 2009年第3期17卷 568-577页

作者： Wang, Yaonan Li, Chunsheng Zuo, Yi Hunan Univ Coll Elect & Informat Technol Changsha 410082 Hunan Peoples R China Guangdong Commercial Coll Dept Math Guangzhou 510320 Guangdong Peoples R China

fuzzy c-means (FCM) and its variants suffer from two problems-local minima and cluster validity-which have a direct impact on the formation of final clustering. There are two strategies-optimization and center initialization strategies-that address the problem of local minima. This paper proposes a center initialization approach based on a minimum spanning tree to keep FCM from local minima. With regard to cluster validity, various strategies have been proposed. On the basis of the fuzzy cluster validity index, this paper proposes a selection model that combines multiple pairs of a fuzzy clustering algorithm and cluster validity index to identify the number of clusters and simultaneously selects the optimal fuzzy clustering for a dataset. The promising performance of the proposed center-initialization method and selection model is demonstrated by experiments on real datasets.

关键词： Cluster validity index competitive comprehensive fuzzy evaluation fuzzy clustering algorithm minimum spanning tree (MST) selection model

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A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation

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SIGNAL PROCESSING 2011年第4期91卷 988-999页

作者： Zhao, Feng Jiao, Licheng Liu, Hanqiang Gao, Xinbo Xidian Univ Inst Intelligent Informat Proc Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian Peoples R China

Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions (GIFP_FCM) is a novel fuzzy clustering algorithm. However when GIFP_FCM is applied to image segmentation, it is sensitive to noise in the image because of ignoring the spatial information contained in the pixels. In order to solve this problem, a novel fuzzy clustering algorithm with non local adaptive spatial constraint (FCA_NLASC) is proposed in this paper. In the proposed method, a novel non local adaptive spatial constraint term is introduced to modify the objective function of GIFP_FCM. The characteristic of this technique is that the adaptive spatial parameter for each pixel is designed to make the non local spatial information of each pixel playing a different role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially magnetic resonance (MR) images, are performed to assess the performance of an FCA_NLASC in comparison with GIFP_FCM and fuzzy c-means clustering algorithms with local spatial constraint. Experimental results show that the proposed method is robust to noise in the image and more effective than the comparative algorithms. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Image segmentation fuzzy clustering algorithm Non local spatial constraint Adaptive spatial parameter Magnetic resonance (MR) image

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