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

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.