Probabilistic Tracking Method Based on the Spatial Bin-Ratio Information
Probabilistic Tracking Method Based on the Spatial Bin-Ratio Information
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DOI：
10.1080/15599612.2011.633208
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发表时间：
2011-10
影响因子：
5.5
通讯作者：
Ruxi Xiang;Jianwei Li;Xuchu Wang;Youjia Fu
中科院分区：
文献类型：
--
作者：
Ruxi Xiang;Jianwei Li;Xuchu Wang;Youjia Fu
提出了一种新的相似性度量方法，该方法可以在概率跟踪器框架内基于粒子滤波对目标进行跟踪。在粒子滤波框架中，选择状态转移模型作为简单的二阶自回归模式，选择状态度量为BBRS （Blocks-Bin-Ratio-Similarity）。BBRS同时考虑直方图的空间信息和bin值之间的比率。通过仿真实验与基于颜色直方图和本比相似度的相似度度量进行比较，视频跟踪结果表明，在具有挑战性的视频中，在外观和运动随时间剧烈变化的情况下，基于bbrs的相似度度量比基于颜色直方图的相似度度量具有更强的判别性。
We proposed a new similarity measure method which could be used within the framework of probability trackers based on the particle filter to track the object. In the particle filter framework, the state transition model is chosen as the simple second-order auto-regressive mode, and the state measure is chosen as the BBRS (Blocks-Bin-Ratio-Similarity). The BBRS considers both the spatial information and the ratios between bin values of histograms. The simulation experiment was made to compare with the similarity measures based on color-histogram and the Bin-Ratio Similarity, and the tracking results in the videos showed that the BBRS-based similarity measure was more discriminative than the color histogram similarity measure in robustly tracking the object in challenging videos where the appearance and motion are drastically changing over time.