CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Real-Time Object Tracking Based on the Relative Hist Model within Particle Filter Framework
Wang, Lingfeng; Pan, Chunhong
2010
Conference NameInternational Conference on Acoustics, Speech, and Signal Processing
Source PublicationInternational Conference on Acoustics, Speech, and Signal Processing
Conference Date2010
Conference PlaceDallas, Texas, USA
AbstractThis paper proposes a novel real-time tracking algorithm by using
the relative hist model within particle filter framework. The target-region
is roughly enclosed with a rectangle as usual, and color features
are used to describe all the types of regions by calculating
their color histograms. Inevitably, the background pixels are included
in the target-region and the histogram of target-region may
be corrupted when performing tracking algorithm. Even the target
fails to track. Thus, the relative hist model is proposed to reduce
the influence of background pixels. In this model, we not only consider
the similarity between the candidate-region and target-region,
but also consider the similarity between the candidate-region and
background. In other words, the relative hist model tries to find a
candidate-region which is more similar to the target-region but less
similar to the background. By adopting this model, our tracking algorithm
can accurately track the object in real-time. Experiments
are performed in various tracking scenes. The experiment results
show that our algorithm is of appealing with respect to robustness
for real-time object tracking against various backgrounds.
KeywordRelative Hist Model Particle Filter
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10767
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Lingfeng,Pan, Chunhong. Real-Time Object Tracking Based on the Relative Hist Model within Particle Filter Framework[C],2010.
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