Aiming to the problem of pedestrian tracking with frequent or long-term occlusion in complex scenes,an anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch is proposed.Fir...Aiming to the problem of pedestrian tracking with frequent or long-term occlusion in complex scenes,an anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch is proposed.Firstly,the occlusion judgment is realized by extracting and utilizing deep feature of pedestrian’s appearance,and then the scale adaptive kernelized correlation filter is introduced to implement pedestrian tracking without occlusion.Secondly,Karman filter is introduced to predict the location of occluded pedestrian position.Finally,the deep feature is used to the rematch of pedestrian in the reappearance process.Simulation experiment and analysis show that the proposed algorithm can effectively detect and rematch pedestrian under the condition of frequent or long-term occlusion.展开更多
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 target.Combining with the cubature Kalman filter with it...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 target.Combining 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 proposed.Firstly,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 update.Secondly,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 step.In 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.展开更多
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 proposed.C...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 proposed.Combined 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 algorithm.In 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 weight.In 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 equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-hne way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a nov...The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
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...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 proposed.Firstly,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 structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In 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 PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.展开更多
基金the National Natural Science Foundation of China(No.61976080,61771006)the Key Project of Henan Province Education Department(No.19A413006).
文摘Aiming to the problem of pedestrian tracking with frequent or long-term occlusion in complex scenes,an anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch is proposed.Firstly,the occlusion judgment is realized by extracting and utilizing deep feature of pedestrian’s appearance,and then the scale adaptive kernelized correlation filter is introduced to implement pedestrian tracking without occlusion.Secondly,Karman filter is introduced to predict the location of occluded pedestrian position.Finally,the deep feature is used to the rematch of pedestrian in the reappearance process.Simulation experiment and analysis show that the proposed algorithm can effectively detect and rematch pedestrian under the condition of frequent or long-term occlusion.
基金Supported by the National Nature Science Foundations of China(No.61300214,U1204611,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+3 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universitiesthe Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘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 target.Combining 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 proposed.Firstly,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 update.Secondly,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 step.In 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.
基金Supported by the National Natural Science Foundation of China(No.61300214)the National Natural Science Foundation of Henan Province(No.132300410148)+1 种基金the Post-doctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher ofHenan Province Universities(No.2013GGJS-026)
文摘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 proposed.Combined 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 algorithm.In 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 weight.In 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 equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-hne way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
基金Supported by the Postdoctoral Science Foundation of China(No.2014M551999)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)
文摘The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
基金Supported by the National Nature Science Foundation of China(No.61300214)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+5 种基金the National Natural Science Foundation of Henan Province(No.132300410148)the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Postdoctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher of Henan Province Universities(No.2013GGJS-026)the Postdoctoral Fund of Henan Province(No.2013029)the Outstanding Young Cultivation Foundation of Henan University(No.0000A40366)
文摘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 proposed.Firstly,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 structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In 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 PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.
