Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability ...Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.展开更多
This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effec...This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is used.Moreover,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data transmission.For the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter gains.Furthermore,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule.As such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation performance.Moreover,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented.Finally,the validity of the developed algorithm is checked using a simulation example.展开更多
文摘Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.
基金Project supported by the National Natural Science Foundation of China(No.12171124)the Natural Science Foundation of Heilongjiang Province of China(No.ZD2022F003)+1 种基金the National High-end Foreign Experts Recruitment Plan of China(No.G2023012004L)the Alexander von Humboldt Foundation of Germany。
文摘This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is used.Moreover,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data transmission.For the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter gains.Furthermore,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule.As such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation performance.Moreover,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented.Finally,the validity of the developed algorithm is checked using a simulation example.
