Support vector tracking
Support vector tracking
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DOI：
10.1109/tpami.2004.53
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发表时间：
2004-08-01
影响因子：
23.6
通讯作者：
Avidan, S
中科院分区：
文献类型：
--
作者：
Avidan, S
支持向量跟踪（SVT）将支持向量机分类器集成到基于光流的跟踪器中。SVT不是最小化连续帧之间的强度差函数，而是最大化SVM分类分数。为了考虑连续帧之间的大运动，我们从支持向量构建金字塔，并在分类阶段使用从粗到精的方法。我们展示了在图像序列中使用SVT进行车辆跟踪的结果。
Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.