Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Generally, particle filters need a large number of particles to approximate the posterior for the purpose of ideal effect. Previous methods extract remarkable particles from the particles at time t-1 by nonlinear function. Those methods use the remarkable particles to reduce the number of particles and improve the accuracy of particle filter. Hover, the nonlinear function extracts the remarkable particles, which will aken or even ignore useful remarkable local particles. Thus this paper presents a new resampling scheme to extract remarkable local particles.  propose a ight threshold and a distance threshold to extract remarkable local particles from particles at time t-1. Meanwhile,  use these remarkable local particles to track the target analytically. Besides,  propose a global transition model to improve the accuracy of the particle filter. Based on remarkable local resampling scheme and the global transition model,  propose a new framework of particle filter. Finally, experiments show that our framework has higher efficiency than previous methods in the case of fer particles.
['Zhiqiang Zhao', 'Tianjiang Wang', 'Fang Liu', 'Gwangmin Choe', 'Caihong Yuan', 'Zongmin Cui']
I am writing a research paper on a idea Remarkable local resampling based on particle filter for visual tracking
[{"from": "human", "value": "I am writing a research paper on a idea Remarkable local resampling based on particle filter for visual tracking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Generally, particle filters need a large number of particles to approximate the posterior for the purpose of ideal effect. Previous methods extract remarkable particles from the particles at time t-1 by nonlinear function. Those methods use the remarkable particles to reduce the number of particles and improve the accuracy of particle filter. Hover, the nonlinear function extracts the remarkable particles, which will aken or even ignore useful remarkable local particles. Thus this paper presents a new resampling scheme to extract remarkable local particles.  propose a ight threshold and a distance threshold to extract remarkable local particles from particles at time t-1. Meanwhile,  use these remarkable local particles to track the target analytically. Besides,  propose a global transition model to improve the accuracy of the particle filter. Based on remarkable local resampling scheme and the global transition model,  propose a new framework of particle filter. Finally, experiments show that our framework has higher efficiency than previous methods in the case of fer particles."}]