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Gaussian mean-shift is an EM algorithm

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年第5期29卷 767-776页

作者： Carreira-Perpinan, Miguel A. Oregon Hlth & Sci Univ Dept Comp Sci & Elect Engn OGI Sch Sci & Engn Beaverton OR 97006 USA

The mean-shift algorithm, based on ideas proposed by Fukunaga and Hostetler [ 16], is a hill-climbing algorithm on the density defined by a finite mixture or a kernel density estimate. mean-shift can be used as a nonparametric clustering method and has attracted recent attention in computer vision applications such as image segmentation or tracking. We show that, when the kernel is Gaussian, mean-shift is an expectation-maximization ( EM) algorithm and, when the kernel is non-Gaussian, mean-shift is a generalized EM algorithm. This implies that mean-shift converges from almost any starting point and that, in general, its convergence is of linear order. For Gaussian mean-shift, we show: 1) the rate of linear convergence approaches 0 ( superlinear convergence) for very narrow or very wide kernels, but is often close to 1 ( thus, extremely slow) for intermediate widths and exactly 1 ( sublinear convergence) for widths at which modes merge, 2) the iterates approach the mode along the local principal component of the data points from the inside of the convex hull of the data points, and 3) the convergence domains are nonconvex and can be disconnected and show fractal behavior. We suggest ways of accelerating mean-shift based on the EM interpretation.

关键词： mean-shift algorithm Gaussian mixtures kernel density estimators EM algorithm clustering

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Study on difference algorithm used in missile radiator real-time tracking

Study on difference algorithm used in missile radiator real-...

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Measurement Touch the World——IEEE 2011 10th International Conference on Electronic Measurement & Instruments(ICEMI’2011)

作者： Xu Zhenhui Yang Xi Jia Yongqian(1 Department of Arms Engineering,Academy of Armored Force Engineering,Beijing,China 2 Military Representation Office of 528 Factory,Nanjing 210006 China)

Due to the bad effect of tracking missile radiator and the problem of the unideal initial position and the severe occlusion based on mean-shift algorithm,it presents a new algorithm named difference algorithm that uses Kalman filter predicting and mean-shift *** to the new difference algorithm method,predicting tracking overall scheme of missile radiator has been proposed and Kalman wave filter has been constructed by using trajectory character of missile *** software is utilized to write the procedure to prove the new *** research result shows that by using difference algorithm,it realizes accurate predict of missile radiator in the next frame *** only the real-time character but also the accuracy character can be improved *** the continual tracking problem of missile radiator after shortly sheltered or missing can be solved effectively.

关键词： missile radiator mean-shift algorithm Kalman filtering difference algorithm

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Two-level automatic multiple target joint tracking and classification

Two-level automatic multiple target joint tracking and class...

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Conference on Signal and Data Processing of Small Targets 2010

作者： He, Xiaofan Tharmarasa, Ratnasingham Pelletier, Michel Kirubarajan, Thia McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada ICx Radar Syst Laval PQ Canada

ISBN: (纸本)9780819481627

Target classification is of great importance for modern tracking systems. The classification results could be fed back to the tracker to improve tracking performance. Also, classification results can be applied for target identification, which is useful in both civil and military applications. While some work has been done on Joint Tracking and Classification (JTC), which can enhance tracking results and make target identification feasible, a common assumption is that the statistical description of classes is predefined or known a prior. This is not true in general. In this paper, two automatic multiple target classification algorithms, which can automatically classify targets without prior information, are proposed. The algorithms learn the class description from the target behavior history. The input to the algorithm is the noisy target state estimate, which in turn depends on target class. Thus, class description is learnt from the target behavior history rather than being predefined. This motivates the proposed two-level tracking and classification formulation for automatic multiple target classification. The first level consists of common tracking algorithm such as the Joint Probability Data Association (JPDA), the Multiple Hypothesis Tracking (MHT) or the Probability Hypothesis Density (PHD) filter. In the second level, a mean-shift (MS) classifier and a PHD classifier are applied to learn the class descriptions respectively based on the state estimations from the first level tracker. The proposed algorithms only require the kinematic measurements from common radar. However, feature information can be easily integrated. Besides theoretical derivations, extensive experiments based on both simulated and real data are performed to verify the efficiency of the proposed technique.

关键词： Classification mean-shift algorithm Probability Hypothesis Density tracking multiple targets

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Robust Object Tracking using Local Oriented Energy Features and its Hardware/Software Implementation

Robust Object Tracking using Local Oriented Energy Features ...

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11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Norouznezhad, Ehsan Bigdeli, Abbas Postula, Adam Lovell, Brian C. NICTA POB 6020 St Lucia Qld 4067 Australia Univ Queensland Sch ITEE Brisbane Qld 4072 Australia

ISBN: (纸本)9781424478132

This paper presents the use of local oriented energy features for real-time object tracking on embedded vision systems. Local oriented energy features are extracted using complex Gabor filters. Filtering is carried out across multiple channels with different frequencies and orientations. The effectiveness of the chosen feature set is tested using a mean-shift tracker. Our experiments show that adding local oriented energy features can significantly enhance the performance of the tracker in presence of photometric variations and geometric transformation. The real-time implementation of the system is also described in this paper. To achieve the desired performance, a hardware/software co-design approach is pursued. Multi-channel Gabor filtering, Local Oriented Energy Feature and Feature histogram Computation is implemented on hardware while mean-shift vector calculation is performed on a processor. The system was synthesized onto a Xilinx Virtex-5 XC5VSX50T using Xilinx ML506 development board and the implementation results are presented.

关键词： Object Tracking Gabor Filters Local Oriented Energy Features mean-shift algorithm Field Programmable Gate Array (FPGA)

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Research on Agricultural Surveillance Video of Intelligent Tracking

Research on Agricultural Surveillance Video of Intelligent T...

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第三届国际计算机与计算技术在农业中的应用研讨会

作者： Lecai Cai Jijia Xu Jin Liangping Zhiyong He Institute of Computer Application Sichuan University of Science and Engineering

Intelligent video tracking technology is the digital video processing and analysis of an important field of application in the civilian and military defense have a wide range of *** this paper,a systematic study on the surveillance video of the Smart in the agricultural tracking,particularly in target detection and tracking problem of the study,respectively for the static background of the video sequences of moving targets detection and tracking algorithm,the goal of agricultural production for rapid detection and tracking algorithm and mean shift-based translation and rotation of the target tracking *** results show that the system can effectively and accurately track the target in the surveillance ***,in agriculture for the intelligent video surveillance tracking study,whether it is from the environmental protection or social security,economic efficiency point of view,are very meaningful.

关键词： video tracking mean-shift algorithm Epanechnikov core density estimation

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Robust Object Tracking using Local Oriented Energy Features and its Hardware/Software Implementation

Robust Object Tracking using Local Oriented Energy Features ...

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International Conference on Control, Automation, Robotics and Vision

作者： Ehsan Norouznezhad Abbas Bigdeli Adam Postula Brian C. Lovell NICTA PO Box 6020 St Lucia QLD 4067 Australia

ISBN: (纸本)9781424478149

关键词： Object Tracking Gabor Filters Local Oriented Energy Features mean-shift algorithm Field Programmable Gate Array (FPGA)

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Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2009年第13期388卷 2717-2726页

作者： Liu, Xuan Song, Weiguo Zhang, Jun Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Peoples R China

In this Study, experiments of single-file pedestrian movement were Conducted and the movement parameters of pedestrians were extracted with a digital image processing method based on a mean-shift algorithm. The microscopic characteristics of pedestrian dynamics, including velocity, density, and lateral oscillation, as well as their interrelations, were obtained and analyzed. Firstly, we studied the lateral oscillation phenomena of pedestrian movement. The result indicates that the trajectory of pedestrians presents a wavy form and the amplitude of the oscillation remains about 5.5 cm when the pedestrians move with free walking velocity, which is the velocity when there is 110 Obvious interaction between sequential pedestrians;but when the movement velocity decreases to 0.27 m/s, the amplitude of oscillation increases to 13 cm. With increasing density, the velocity decreases and the amplitude of oscillation presents a linear increase trend. The increasing oscillation amplitude widens the Occupation area of a pedestrian with low velocity, so as to make the moving efficiency even worse. Secondly. we Studied the frequency of the oscillation;the result indicates that the frequency remains at 2 Hz when pedestrians move with a free walking velocity, but it presents a similar linear decrease trend when the velocity changes to a lower value. The decrease of oscillation frequency is also a negative feedback to the moving efficiency. Thirdly, it is found that with the increase of crowd density, the time interval between two sequential pedestrians increases, though the space gap between them decreases. The quantitative relation between time interval and crowd density is obtained. The study in this paper provides fundamental data and a basic method for understanding pedestrian dynamics, developing and validating evacuation models. The results are also expected to be useful for evacuation design. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Evacuation Pedestrian flow Digital image processing mean-shift algorithm Experiment

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An Automatic Food Recognition algorithm with both Shape and Texture Information

An Automatic Food Recognition Algorithm with both Shape and ...

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Conference on PIAGENG - Image Processing and Photonics for Agricultural Engineering

作者： Deng, Yu Qin, Shiyin Wu, Yunjie BeiHang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9780819478009

Automatic food classification with digital images has played an important role in modern agricultural and food engineering. For this purpose, a kind of recognition algorithm for food is presented based on their shape and texture information in this paper.. By using a combination of shape and texture feature, improved mean-shift procedure is a state-of-the-art learning algorithm for multi-classification of food. The proposed method has four steps: (1) computation of a high contrast monochrome image from an optimal linear combination of RGB components of the food colour image;(2) a morphological shape detection operation is applied to detect the actual food shape from the high contrast monochrome image, some structural elements that have special forms are utilized to eliminate noise and improve detection precision;(3) a food texture is modeled by co-occurrence matrix;(4) a feature combination method is specified by food shape and texture information synthetically, then an improved mean-shift algorithm is proposed to achieve automatic food classification and recognition. The algorithm was implemented in Matlab and tested with 180 images (512x512) taken for various food with big differences. The algorithm can be applied to recognize food categories at the speed of 1.13s per image with the approval recognition rate of 97.6%. The result shows that our algorithm fully satisfies the requests of real application.

关键词： Food recognition Morphological shape detection Co-occurrence matrix Feature combination mean-shift algorithm

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mean-shift Tracker with Face-adjusted Model

Mean-shift Tracker with Face-adjusted Model

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International Conference on Control, Automation and Systems

作者： Choe, Jeehyun Goya Seok, Joon-Hong Lee, Ju-Jang Korea Adv Inst Sci & Technol Robot Program Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Daejeon South Korea

ISBN: (纸本)9788995003893

mean-shift algorithm shows robust performances in various object-tracking technologies including face tracking. Due to its robustness and accuracy, mean-shift algorithm is regarded as one of the best ways to apply in object-tracking technology in computer vision fields. However, it has a drawback of getting into a bottleneck state when faced with a speedy object moving beyond its window size within one image frame interval time. The time required to calculate mean-shift vector could be much lessened with lesser memory when color model is adjusted to the previously known target information. This paper shows the building process of target-adjusted model with a non-uniform quantization. The target color model dealt in this paper is the one used for deriving mean-shift vector. It is a kernel model containing both the color and distance information. This paper gives scheme to efficiently deal with color information in the model. Through a proper selection of color bins, unimportant color values were reduced to a small amount. As a result, the computing time of the mean-shift vector in face-tracking was shortened while maintaining robustness and accuracy.

关键词： Face tracking mean-shift algorithm Face-adjusted Model LUT(Look Up Table)

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A MOTION TRACKING METHOD BASED ON KALMAN FILTER COMBINED WITH mean-shift

A MOTION TRACKING METHOD BASED ON KALMAN FILTER COMBINED WIT...

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6th International Conference on Wavelet Analysis and Pattern Recognition

作者： Zhao, Jie Liu, Wei-Jing Sun, Hui-Jia Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China

ISBN: (纸本)9781424422388

In this paper, it proposes an object tracking algorithm based-on the Kalman filter combined with the mean-shift algorithm. It can predict the object motion more accurate with Kalman filter, including position and velocity. And the adjacent locations of the predicted point are defined as the search window. In the search window, the position of object is fixed on by mean-shift. The experiment results show that this algorithm can make full use of the prediction function of Kalman filter, improve the search speed, and achieve a more accurate tracking even the color is similar, and also solve the problem of shelter to some extent.

关键词： Kalman filter mean-shift algorithm Tracking Prediction

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