Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabili...
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Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is ***,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in *** the object is to further improve state estimation precision of nonlinear ***,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo *** addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of ***,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.
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 ...
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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.
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 assum...
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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.
We developed a novel method named MemBrain-TMB to predict the spanning segments of transmembrane Â-barrel from amino acid sequence. MemBrain-beta is a statistical machine learningbased model, which is constructed...
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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 t...
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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.
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...
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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.
This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to reco...
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This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to recover the coordinates of 3D points from 2D images by the related coordinate conversion factor (CCF). The images are captured by a monocular camera. Second, the human body is represented by a connected three-stick model. Then the parameters of the body model are recovered by the method of projective geometry using the related CCF. Finally, the gait feature composed of those parameters is defined, and it is proved by experiments that those features can partially avoid the influence of viewing angles between the optical axis of the camera and walking direction of the subject.
To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel...
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To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel saliency model is proposed to distinguish the salient target from background, and the eigenspace model is invoked to adapt target appearance changes. To account for the abrupt motions efficiently, a two- step sampling method is proposed to combine the two observation models. The proposed tracking method is demonstrated through two real infrared image sequences, which include the changes of luminance and size, and the drastic abrupt motions of the target.
In this paper we present a new shape normalization method that is invariant to shape translation, rotation and scaling. We define a visible area density function and an unvisible area density function for a planar sha...
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In this paper we present a new shape normalization method that is invariant to shape translation, rotation and scaling. We define a visible area density function and an unvisible area density function for a planar shape. Using these two functions we define a visible region center and an unvisible region center of the shape, respectively. When the visible and unvisible region centers of a shape locate at different positions they can be utilized as characteristic points to normalize the shape to a standard form. The normalizing process by use of the centers is presented. Experiments are executed on five groups of shapes with distortion of translation, rotation and scaling adding quantilization noise. The results show that the method is reasonable and available.
The mobile object (MO) location determination technologies which can be used in intelligent transportation system (ITS) are studied in this paper. The principles and characteristics of wireless location determination ...
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The mobile object (MO) location determination technologies which can be used in intelligent transportation system (ITS) are studied in this paper. The principles and characteristics of wireless location determination technologies are introduced and the characteristics of GSM useful for location determination are also summarized. An experimental positioning system based on GSM is proposed, and the architecture is described. TOA method based on GSM signals and TDOA method are used in the experimental system. Moreover, the methods are simulated. The performance of the positioning methods is assessed in the simulation environment, and the accuracy for 67% mobile stations (MS) is 70m in urban areas.
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