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Orbital reference frame estimation with power spectral density constraints for drag-free satellites 认领 引用 被引量：5
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作者 Zhang Yonghe Wang Yamin +1 位作者 Mao Qingyun Liang Xuwen 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2016年第6期1721-1729,共9页
The drag-free satellites are widely used in the field of fundamental science as they enable the high-precision measurement in pure gravity fields. This paper investigates the estimation of local orbital reference fram... The drag-free satellites are widely used in the field of fundamental science as they enable the high-precision measurement in pure gravity fields. This paper investigates the estimation of local orbital reference frame(LORF) for drag-free satellites. An approach, taking account of the combination of the minimum estimation error and power spectral density(PSD) constraint in frequency domain, is proposed. Firstly, the relationship between eigenvalues of estimator and transfer function is built to analyze the suppression and amplification effect on input signals and obtain the eigenvalue range. Secondly, an optimization model for state estimator design with minimum estimation error in time domain and PSD constraint in frequency domain is established. It is solved by the sequential quadratic programming(SQP) algorithm. Finally, the orbital reference frame estimation of low-earth-orbit satellite is taken as an example, and the estimator of minimum variance with PSD constraint is designed and analyzed using the method proposed in this paper. 展开更多
关键词 Drag-free satellite Orbital reference frame Power spectral density State estimation Transfer function
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Visualising data distributions with kernel density estimation and reduced chi-squared statistic 认领 引用 被引量：9
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作者 C.J.Spencer C.Yakymchuk M.Ghaznavi 《Geoscience Frontiers》 SCIE CAS CSCD 2017年第6期1247-1252,共6页
The application of frequency distribution statistics to data provides objective means to assess the nature of the data distribution and viability of numerical models that are used to visualize and interpret data.Two c... The application of frequency distribution statistics to data provides objective means to assess the nature of the data distribution and viability of numerical models that are used to visualize and interpret data.Two commonly used tools are the kernel density estimation and reduced chi-squared statistic used in combination with a weighted mean.Due to the wide applicability of these tools,we present a Java-based computer application called KDX to facilitate the visualization of data and the utilization of these numerical tools. 展开更多
关键词 Data visualisation Kernel density estimation Reduced chi-squared statistic Mean square weighted deviation Geostatistics
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Video Scene Invariant Crowd Density Estimation Using Geographic Information Systems 认领 引用 被引量：4
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作者 SONG Hongquan LIU Xuejun +2 位作者 LU Guonian ZHANG Xingguo WANG Feng 《China Communications》 SCIE CSCD 2014年第11期80-89,共10页
Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation ... Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation method using Geographic Information Systems(GIS) to monitor crowd size for large areas.The proposed method mapped crowd images to GIS.Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera.Test results show that one model obtained by one camera in GIS can be adaptively applied to other cameras in outdoor video scenes.A real-time monitoring system for crowd size in large areas based on scene invariant model has been successfully used in 'Jiangsu Qinhuai Lantern Festival,2012'.It can provide early warning information and scientific basis for safety and security decision making. 展开更多
关键词 crowd density estimation videoscene invariant GIS video spatial registration
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Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets 认领 引用 被引量：3
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作者 Yang Jinlong Yang Le +1 位作者 Yuan Yunhao Ge Hongwei 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2016年第6期1740-1748,共9页
The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledg... The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches. 展开更多
关键词 Adaptive parameter estimation Multiple target tracking Multivariate Gaussian distribution Particle filter Probability hypothesis density
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Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation 认领 引用 被引量：16
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作者 熊丽 梁军 钱积新 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第4期524-532,共9页
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t... Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator. 展开更多
关键词 multivariate statistical process monitoring principal comPonent analysis kermel density estimation polypropylene catalyzer reactor fault detection data-driven tools
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Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation 认领 引用 被引量：5
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作者 Peizhe Xin Ying Liu +2 位作者 Nan Yang Xuankun Song Yu Huang 《Global Energy Interconnection》 EI 2020年第3期247-258,共12页
In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling met... In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE. 展开更多
关键词 Moving average method Signal decomposition Wind power fluctuation characteristics Kernel density estimation Constrained order optimization
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Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach 认领 引用 被引量：8
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作者 Jie Chen Huiqiong Huang +4 位作者 Yichao Rui Yuanyuan Pu Sheng Zhang Zheng Li Wenzhong Wang 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2024年第7期943-956,共14页
Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust l... Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications. 展开更多
关键词 Microseismic source/acoustic emission(MS/AE) Kernel density estimation(KDE) Damping linear correction Source location Abnormal arrivals
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Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) for evaluation of early bone mass changes in ageing osteoporosis patients 认领 引用
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作者 Yonggui Yang Fang Chen +2 位作者 Xiufen Wu Xinyu Xu Pu-Yeh Wu 《Magnetic Resonance Letters》 EI CAS 2025年第2期11-18,共8页
This study explored the application value of iterative decomposition of water and fatwith echo asymmetry and least-squares estimation(IDEAL-IQ)technology in the early diagnosis of ageing osteoporosis(OP).172 participa... This study explored the application value of iterative decomposition of water and fatwith echo asymmetry and least-squares estimation(IDEAL-IQ)technology in the early diagnosis of ageing osteoporosis(OP).172 participants were enrolled and underwentmagnetic resonance imaging(MRI)examinations on a 3.0T scanner.100 cases were included in the normal group(50 males and 50 females;mean age:45 years;age range:20e84 years).33 cases were included in the osteopenia group(17 males and 16 females;mean age:55 years;age range:43e83 years).39 caseswere includedintheOP group(19males and20females;meanage:58years;age range:48 e82 years).Conventional T1WI and T2WI were first obtained,followed by 3D-IDEAL-IQ-acqui-sition.Fat fraction(FF)and apparent transverse relaxation rate(R2*)resultswere automatically calculated from IDEAL-IQ-images on the console.Based on T1Wand T2W-images,300 ROIs for each participantweremanually delineated in L1-L5 vertebral bodies of five middle slices.In each age group of all normal subjects,each parameter was significantly correlated with gender.In male participants from the normal,osteopenia,and OP groups,statistical analysis revealed F values of 11319.292 and 180.130 for comparisons involving FF and R2*values,respectively(all p<0.0001).The sensitivity and specificity of FF values were 0.906 and 0.950,0.994 and 0.997,0.865 and 0.820,respectively.For R2*,they were 0.665 and 0.616,0.563 and 0.519,0.571 and 0.368,respectively.In female participants from the normal,osteopenia,and OP-groups,statis-tical analysis revealed F values of 12461.658 and 548.274 for comparisons involving FF and R2*values,respectively(all p<0.0001).The sensitivity and specificity of FF values were 0.985 and 0.991,0.996 and 0.996,0.581 and 0.678,respectively.For R2*,they were 0.698 and 0.730,0.603 and 0.665,0.622 and 0.525,respectively.Significant differences were indicated in the quanti-tative values among the three groups.FF value had good performance,while R2*value had poor performance indiscriminatingosteopenia andOP-groups.Overall,the IDEAL-IQ techniqueoffers specific reference indices that enable noninvasive and quantitative assessment of lumbar vertebrae bone metabolism,thereby providing diagnostic information for OP. 展开更多
关键词 Magnetic resonance imaging Iterative decomposition of water and fat with echo asymmetry and least-squares estimation Bone mineral density Osteoporosis Osteopenia
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Data-Based Optimal Bandwidth for Kernel Density Estimation of Statistical Samples 认领 引用 被引量：4
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作者 Zhen-Wei Li Ping He 《Communications in Theoretical Physics》 SCIE CAS CSCD 2018年第12期728-734,共7页
It is a common practice to evaluate probability density function or matter spatial density function from statistical samples. Kernel density estimation is a frequently used method, but to select an optimal bandwidth o... It is a common practice to evaluate probability density function or matter spatial density function from statistical samples. Kernel density estimation is a frequently used method, but to select an optimal bandwidth of kernel estimation, which is completely based on data samples, is a long-term issue that has not been well settled so far. There exist analytic formulae of optimal kernel bandwidth, but they cannot be applied directly to data samples,since they depend on the unknown underlying density functions from which the samples are drawn. In this work, we devise an approach to pick out the totally data-based optimal bandwidth. First, we derive correction formulae for the analytic formulae of optimal bandwidth to compute the roughness of the sample's density function. Then substitute the correction formulae into the analytic formulae for optimal bandwidth, and through iteration we obtain the sample's optimal bandwidth. Compared with analytic formulae, our approach gives very good results, with relative differences from the analytic formulae being only 2%～3% for sample size larger than 10~4. This approach can also be generalized easily to cases of variable kernel estimations. 展开更多
关键词 numerical methods kernel density estimation optimal bandwidth large-scale structure of Universe
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Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method 认领 引用 被引量：6
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作者 ZHANG Baoqiang CHEN Guoping GUO Qintao 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2012年第6期879-886,共8页
An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only smal... An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples. 展开更多
关键词 model validation small samples uncertainty analysis kernel density estimation confidence level prediction
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Bioinspired polarized light compass in moonlit sky for heading determination based on probability density estimation 认领 引用 被引量：2
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作者 Yueting YANG Yan WANG +4 位作者 Lei GUO Bo TIAN Jian YANG Wenshuo LI Taihang CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第3期1-9,共9页
Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the ... Bioinspired polarized skylight navigation,which can be used in unfamiliar territories,is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System(GNSS).However,the polarization pattern in night environment with noise effects and model uncertainties is a less explored area.Although several decades have passed since the first publication about the polarization of the moonlit night sky,the usefulness of nocturnal polarization navigation is only sporadic in previous researches.This study demonstrates that the nocturnal polarized light is capable of providing accurate and stable navigation information in dim light outdoor environment.Based on the statistical characteristics of Angle of Polarization(Ao P)error,a probability density estimation method is proposed for heading determination.To illustrate the application potentials,the simulation and outdoor experiments are performed.Resultingly,the proposed method robustly models the distribution of Ao P error and gives accurate heading estimation evaluated by Standard Deviation(STD)which is 0.32°in a clear night sky and 0.47°in a cloudy night sky. 展开更多
关键词 Nocturnal polarization Moonlit sky Angle of Polarization(AoP) Probability density estimation Navigation
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Statistical Characteristics Analysis Based on F/A-XX Fighter Using Adapative Kernel Density Estimation Algorithm 认领 引用 被引量：3
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作者 FU Li JIANG Guanwu HUANG Quanjun 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第6期1202-1210,共9页
The sixth-generation fighter has superior stealth performance,but for the traditional kernel density estimation(KDE),precision requirements are difficult to satisfy when dealing with the fluctuation characteristics of... The sixth-generation fighter has superior stealth performance,but for the traditional kernel density estimation(KDE),precision requirements are difficult to satisfy when dealing with the fluctuation characteristics of complex radar cross section(RCS).To solve this problem,this paper studies the KDE algorithm for F/AXX stealth fighter.By considering the accuracy lack of existing fixed bandwidth algorithms,a novel adaptive kernel density estimation(AKDE)algorithm equipped with least square cross validation and integrated squared error criterion is proposed to optimize the bandwidth.Meanwhile,an adaptive RCS density estimation can be obtained according to the optimized bandwidth.Finally,simulations verify that the estimation accuracy of the adaptive bandwidth RCS density estimation algorithm is more than 50%higher than that of the traditional algorithm.Based on the proposed algorithm(i.e.,AKDE),statistical characteristics of the considered fighter are more accurately acquired,and then the significant advantages of the AKDE algorithm in solving cumulative distribution function estimation of RCS less than 1 m2 are analyzed. 展开更多
关键词 radar cross section(RCS) kernel density estimation(KDE) statistical properties
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An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City 认领 引用 被引量：1
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作者 Yu Zhou Bosong Lin +1 位作者 Siqi Hu Dandan Yu 《Computers, Materials & Continua》 SCIE EI 2024年第4期161-182,共22页
This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating populatio... This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban planning.However,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction.To address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data.The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift.Further,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population predictions.Comparative experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction assignments.The performance of this model in tasks pertaining to population prediction exhibits commendable results. 展开更多
关键词 Population density estimation smart city transformer multiview learning
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An Independent Component Analysis Algorithm through Solving Gradient Equation Combined with Kernel Density Estimation 认领 引用 被引量：2
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作者 薛云峰 王宇嘉 杨杰 《Journal of Shanghai Jiaotong university(Science)》 EI 2009年第2期204-209,共6页
A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introdu... A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm. 展开更多
关键词 independent component analysis blind source separation gradient method kernel density estimation
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MULTITARGET STATE AND TRACK ESTIMATION FOR THE PROBABILITY HYPOTHESES DENSITY FILTER 认领 引用 被引量：3
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作者 Liu Weifeng Han Chongzhao +2 位作者 Lian Feng Xu Xiaobin Wen Chenglin 《Journal of Electronics(China)》 2009年第1期2-12,共11页
The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existi... The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation. 展开更多
关键词 Probability Hypotheses Density （PHD） Particle-PHD filter State and track estimation Finite mixture models
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Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking 认领 引用 被引量：3
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作者 张路平 王鲁平 +1 位作者 李飚 赵明 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第3期956-965,共10页
In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis ... In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD. 展开更多
关键词 particle filter with probability hypothesis density marginalized particle filter meanshift kernel density estimation multi-target tracking
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ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION 认领 引用 被引量：2
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作者 陈斌 李斌 +1 位作者 冯爱民 潘志松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期275-281,共7页
One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of t... One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships. 展开更多
关键词 one-class support vector machine（OCSVM） support vector data description（SVDD） kernel density estimation
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Video-Based Crowd Density Estimation and Prediction System for Wide-Area Surveillance 认领 引用 被引量：2
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作者 曹黎俊 黄凯奇 《China Communications》 SCIE CSCD 2013年第5期79-88,共10页
Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In... Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method. 展开更多
关键词 crowd density estimation prediction system AMID visual surveillance
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Probability density function and estimation for error of digitized map coordinates in GIS 认领 引用 被引量：1
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作者 童小华 刘大杰 《Journal of Central South University of Technology》 2004年第1期69-74,共6页
Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in... Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in GIS does not obey the normal distribution but the p-norm distribution with a determinate parameter. Assuming that the error is random and has the same statistical properties, the probability density function of the normal distribution, Laplace distribution and p-norm distribution are derived based on the arithmetic mean axiom, median axiom and p-median axiom, which means that the normal distribution is only one of these distributions but not the least one. Based on this ideal distribution fitness tests such as Skewness and Kurtosis coefficient test, Pearson chi-square chi(2) test and Kolmogorov test for digitized data are conducted. The results show that the error in map digitization obeys the p-norm distribution whose parameter is close to 1.60. A least p-norm estimation and the least square estimation of digitized data are further analyzed, showing that the least p-norm adjustment is better than the least square adjustment for digitized data processing in GIS. 展开更多
关键词 probability density function distribution fitness test least p-norm estimation
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Diversity Sampling Based Kernel Density Estimation for Background Modeling 认领 引用
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作者 毛燕芬 施鹏飞 《Journal of Shanghai University(English Edition)》 2005年第6期506-509,共4页
A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for ... A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems. 展开更多
关键词 background subtraction diversity sampling kernel density estimation multi-modal background model
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