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Información general |
| Autor |
Wang, Hui-bin; Shen, Jie; Chen, Zhe; Shen, Jun-lei |
| Publicado |
InTech Open Access Publisher, 2013
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| Abstract |
Particle set sampling and weighting are both at
the core of particle filter‐based object tracking methods.
Aiming to optimally represent the objectʹs motion state, a
large amount of particles ‐ in the classical particle method ‐
is a prerequisite. The high‐cost calculation of these particles
significantly slows down the convergence of the algorithm.
To this problem, a prior approach which originated from
the process of video compressing and uncompressing is
introduced to optimize the phase of particle sampling,
making the collected particles centre on and cover the
object region in the current image. This advantage
dramatically reduces the number of particles required by
the regularized particle sampling method, solving the
problem of the high computational cost for tracking
objects, while the performance of the algorithm is stable. |
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International Journal of Advanced Robotic Systems
Autor: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publicado: 2004
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