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Informations générales |
| Auteur |
Xiaowei, Zhang; Liu, Hong; Xiaohong, Sun |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
Particle filter algorithms are widely used for
object tracking in video sequences, but the standard
particle filter algorithm cannot solve the validity of
particles ideally. To solve the problems of particle
degeneration and sample impoverishment in a particle
filter tracking algorithm, an improved object tracking
algorithm is proposed, which combines a multi‐feature
fusion method and a genetic evolution mechanism. The
algorithm dynamically computes the feature’s fusion
weight by the discriminability of each vision feature and
then constructs the important density function based on
selecting a feature’s fusion method adaptively. Moreover,
a self‐adaptive genetic evolutionary mechanism is
introduced into the particle resampling process and
makes the particle become an agent with the ability of
dynamic self‐adaption. With self‐adaptive crossover and
mutation operators, the evolution system produces a
large number of new particles, which can better
approximate the true state of the tracking object. The
experimental results show that the proposed object
tracking algorithm surpasses the conventional particle
filter on both robustness and accuracy, even though the
tracking object is very challenging regarding illumination
variation, structural deformation, |
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International Journal of Advanced Robotic Systems
Auteur: 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, [...]
Publié: 2004
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