资源分类
》 资源 》论文》Enhancing Particle Filters Using Local Likelihood Sampling

Enhancing Particle Filters Using Local Likelihood Sampling

2020-03-25 | |  61 |   40 |   0

Abstract

Particle filters provide a means to track the state of an ob- ject even when the dynamics and the observations are non-linear/non- Gaussian. However, they can be very ineficient when the observation noise is low as compared to the system noise, as it is often the case in visual tracking applications. In this paper we propose a new two-stage sampling procedure to boost the performance of particle filters under this condition. We provide conditions under which the new procedure is proven to reduce the variance of the weights. Synthetic and real-world visual tracking experiments are used to confirm the validity of the theo- retical analysis.

上一篇：Bias in the Localization of Curved Edges

下一篇：Texture Boundary Detection for Real-Time Tracking

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...