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可变形零件模型的级联目标检测
Cascade Object Detection with Deformable Part Models |
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| 课程网址: |
http://videolectures.net/cvpr2010_girshick_codd/
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| 主讲教师: |
Ross B. Girshick
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| 开课单位: |
脸书公司
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| 开课时间: |
2010-07-19
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| 课程语种: |
英语
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| 中文简介: |
我们描述了从基于部件的可变形模型（例如图形结构）构建级联分类器的一般方法。我们主要关注星型结构模型的情况，并展示基于部分假设修剪的简单算法如何在不牺牲检测精度的情况下将对象检测加速超过一个数量级。在我们的算法中，部分假设通过一系列阈值来实现。类似于可能近似校正（PAC）学习，我们引入了可能近似可接受（PAA）阈值的这种阈值。这些阈值为级联方法的性能提供了理论上的保证，并且可以从少量的正例中计算出来。最后，我们概述了由语法形式定义的一般类型模型的级联检测算法。这个类不仅包括树形结构的图像结构，还包括可以递归地将每个部分表示为其他部分的混合的alsoricher模型。
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| 课程简介: |
We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured models and show how a simple algorithm based on partial hypothesis pruning can speed up object detection by more than one order of magnitude without sacrificing detection accuracy. In our algorithm, partial hypotheses are pruned with a sequence of thresholds. In analogy to probably approximately correct (PAC) learning, we introduce the notion of probably approximately admissible (PAA) thresholds. Such thresholds provide theoretical guarantees on the performance of the cascade method and can be computed from a small sample of positive examples. Finally, we outline a cascade detection algorithm for a general class of models defined by a grammar formalism. This class includes not only tree-structured pictorial structures but also richer models that can represent each part recursively as a mixture of other parts.
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| 关 键 词: |
可变形模型; 星型结构模型; 阈值
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-01-13：chenxin
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| 阅读次数: |
60
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