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Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

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High Technology Letters 2015年第1期21卷 39-45页

作者：胡振涛 Fu Chunling Cao Zhiwei Li Congcong instituteof image processing and pattern recognition Henan University School of Physics and Electronics Henan University

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering *** with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is ***,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation ***,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering *** the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

关键词： maneuvering target tracking nonlinear filtering cubature Kalman filter(CKF) interacting multiple model(IMM)

Pose estimation based on human detection and segmentation

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Science in China(Series F) 2009年第2期52卷 244-251页

作者： CHEN Qiang ZHENG EnLiang LIU YunCai institute of image processing and pattern recognition Shanghai Jiao Tong University Shanghai 200240 China

We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption, In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.

关键词： human detection and segmentation 3D pose estimation regression machine learning

Nonnegative matrix factorization with Log Gabor wavelets for image representation and classification

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Journal of Systems Engineering and Electronics 2005年第4期16卷 738-745页

作者： Zheng Zhonglong Yang Jie inst. of image processing and pattern recognition of Shanghai Jiaotong Univ. Shanghai 200030 P. R. China

Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially localized, partsbased subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis （PCA） and locally linear embedding （LIE） proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied.

关键词： non-negative matrix factorization (NMF) Log Gabor wavelets principal component analysis locally linearembedding (LLE)

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

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High Technology Letters 2014年第4期20卷 355-362页

作者：胡振涛 Fu Chunling institute of image processing and pattern recognition Henan University School of Physics and Electronics Henan University

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is *** with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new *** the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle *** addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement *** the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor ***,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： multi-sensor information fusion weight optimization correlated noises maneuvering target tracking

Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion

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High Technology Letters 2016年第4期22卷 376-384页

作者：胡振涛 Hu Yumei Guo Zhen Wu Yewei institute of image processing and pattern recognition Henan University College of Automation Northwestern Polytechnical University

The GM-PHD framework as recursion realization of PHD filter is extensively applied to multitarget tracking system. A new idea of improving the estimation precision of time-varying multi-target in non-linear system is proposed due to the advantage of computation efficiency in this paper. First,a novel cubature Kalman probability hypothesis density filter is designed for single sensor measurement system under the Gaussian mixture framework. Second,the consistency fusion strategy for multi-sensor measurement is proposed through constructing consistency matrix. Furthermore,to take the advantage of consistency fusion strategy,fused measurement is introduced in the update step of cubature Kalman probability hypothesis density filter to replace the single-sensor measurement. Then a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed. Capabilily of the proposed algorithm is illustrated through simulation scenario of multi-sensor multi-target tracking.

关键词： multi-target tracking probability hypothesis density(PHD) cubature Kalman filter consistency fusion

Color-texture based unsupervised segmentation using JSEG with fuzzy connectedness

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Journal of Systems Engineering and Electronics 2006年第1期17卷 213-219页

作者： Zheng Yuanjie Yang Jie Zhou Yue Wang Yuzhong inst. of image processing and pattern recognition Shanghai Jiaotong Univ. Shanghai 200030 P. R. China

Color quantization is bound to lose spatial information of color distribution. If too much necessary spatial distribution information of color is lost in JSEG, it is difficult or even impossible for JSEG to segment image correctly. Enlightened from segmentation based on fuzzy theories, soft class-map is constracted to solve that problem. The definitions of values and other related ones are adjusted according to the soft class-map. With more detailed values obtained from soft class map, more color distribution information is preserved. Experiments on a synthetic image and many other color images illustrate that JSEG with soft class-map can solve efficiently the problem that in a region there may exist color gradual variation in a smooth transition. It is a more robust method especially for images which haven＇ t been heavily blurred near boundaries of underlying regions.

关键词： unsupervised segmentation color segmentation color texture segmentation fuzzy method.

Segmentation of the Ventricle Membranes in Short-Axis Sequences by Optical Flow Base on DLSRE Model

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Chinese Journal of Electronics 2021年第3期30卷 460-470页

作者： LI Lin WU Hengfei LI Junhua Department of Electronics and Information Engineering Bozhou University Key Laboratory of Jiangxi Province for image processing and pattern recognition Nanchang Hangkong University

Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution(DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.

关键词： Optical, image and video signal processing Biomedical magnetic resonance imaging and spectroscopy Computer vision and image processing techniques Medical magnetic resonance imaging and spectroscopy Patient diagnostic methods and instrumentation Biology and medical computing Double-level set region evolution Optical flow Left ventricle image segmentation

Study on Performances of Car-following Models Induced by Motions of a Leading Car

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Journal of Shanghai Jiaotong university(Science) 2005年第3期10卷 217-220,225页

作者：李莉施鹏飞 inst. of image processing and pattern recognition Shanghai Jiaotong Univ. Sha nghai 200030 China

This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specilied speed profile was used to test the above model. The results show that this model is to some extent deficient in performing the process aforementioned. Modifications of the model to overcome these deficiencies were demonstrated anda modified car-following model was proposed accordingly. Furthermore, the delay time of car motion of the new model were studied.

关键词： car-following models leading car linear stability, delay time

Probabilistic Model-Based Silhouette Refinement for Gait recognition

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Journal of Shanghai Jiaotong university(Science) 2010年第1期15卷 24-30页

作者：张元元吴晓娟阮秋琦 institute of image processing and pattern recognition Shandong University institute of Information Science Beijing Jiaotong University

An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed,and a relatively complete and high quality silhouette is *** silhouettes are sequentially refined in two levels according to two different probabilistic *** first level is within-sequence *** silhouette in a particular sequence is refined by an individual model trained by the gait images from current *** second level is between-sequence *** the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of *** intention is to preserve the within-class similarity and to decrease the interaction between one class and *** experimental results indicate that the proposed algorithm is simple and quite effective,and it helps the existing recognition methods achieve a higher recognition performance.

关键词： silhouette refinement probabilistic model gait recognition performance evaluation