Improvement of Maximum Variance Weight Partitioning Particle Filter in Urban Computing and Intelligence

Li Huang,Disi Chen,Gongfa Li,Hui Yu,Bowen Luo,Qiaobo Fu

doi:10.1109/access.2019.2932144

Abstract

At present, urban computing and intelligence has become an important topic in the research field of artificial intelligence. On the other hand, computer vision as a crucial bridge between urban world and artificial intelligence is playing a key role in urban computing and intelligence. Conventional particle filter is derived from Karman filter, which theoretically based on Monte Carlo method. Sequential importance resampling (SIR) is implemented in conventional particle filter to avoid the degeneracy problem. In order to overcome the shortcomings of the resampling algorithm in the traditional particle filter, we proposed an optimized particle filter using the maximum variance weight segmentation resampling algorithm in this paper, which improved the performance of particle filter. Compared with the traditional particle filter algorithm, the experimental results show that the proposed scheme outperforms in terms of computational consumption and the accuracy of particle tracking. The final experimental results proved that the quality of the maximum variance weight segmentation method increased the accuracy and stability in motion trajectory tracking tasks.

Highlights

The emerging of the first applicable Particle filter algorithm raised the research trend of Particle filter until now [1]
Considering the shortcomings of traditional particle filter [4], we proposed the improved Particle filter in this paper and the experimental results proves that the effectiveness of each stage in hand motion tracking [5]
It is theoretically and experimentally proved that the resampling method with maximum variance weight segmentation improves the performance of traditional particle filter algorithm [11], which saves computational time and improves the accuracy of in real-time tracking [12]

Summary

INTRODUCTION

The emerging of the first applicable Particle filter algorithm raised the research trend of Particle filter until now [1]. Considering the shortcomings of traditional particle filter [4], we proposed the improved Particle filter in this paper and the experimental results proves that the effectiveness of each stage in hand motion tracking [5]. L. Huang et al.: Improvement of Maximum Variance Weight Partitioning Particle Filter accuracy, real-time capability and the self-adaptability [8]. It is theoretically and experimentally proved that the resampling method with maximum variance weight segmentation improves the performance of traditional particle filter algorithm [11], which saves computational time and improves the accuracy of in real-time tracking [12]. Aiming at the shortcomings of resampling algorithm in traditional particle filter method, a resampling algorithm based on maximum variance weight segmentation is proposed, and an improved algorithm of particle filter is given. The fourth part introduces the recognition experiment based on the algorithm and summarizes the experimental

RELATED WORK

POSTERIOR ESTIMATION USING MONTE CARLO METHOD

EXPERIMENTAL RESULTS ANALYSIS

CONCLUSION