Please use this identifier to cite or link to this item:
|Title:||A particle filtering framework with indirect measurements for visual tracking|
|Source:||Zhang, H.,Huang, W.,Huang, Z.,Zhang, B. (2004). A particle filtering framework with indirect measurements for visual tracking. 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV) 1 : 723-728. ScholarBank@NUS Repository.|
|Abstract:||Particle filtering is a stochastic approach to Bayesian recursive inference. In many computer vision applications with limited number of random samples, however, conventional particle filters may find it difficult to accurately represent the desired aposteriori distribution especially for target objects with narrow likelihood functions. This paper proposes a new particle filtering framework which, by incorporating a special indirect measurement model, can significantly improve the representation capability of the particle set, yielding an accurate estimation of aposteriori distribution for the purpose of tracking. In particular, an add-on resampling technique is proposed to incorporate the indirect measurement. In this way, we can alleviate the problem with large numbers of particles required in conventional particle filtering. Positive experimental results on both synthetic sequences and real world videos are obtained. © 2004 IEEE.|
|Source Title:||2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.