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基于支持向量机的目标检测算法综述
  • 期刊名称:控制欲决策
  • 时间:2014.2.2
  • 页码:193-200
  • 分类:TP273[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
  • 作者机构:[1]中国科学技术大学自动化系,合肥230027
  • 相关基金:国家自然科学基金项目(61005091,61375079).
  • 相关项目:基于视知觉显著性学习的运动目标鲁棒跟踪研究
中文摘要:

目标检测的目的在于从静态图片或视频中检测并定位设定种类的目标物体,已有研究大都将目标检测问题简化为一个二分类问题.鉴于支持向量机在模式识别领域尤其是解决二分类问题中所表现出来的优越性,如何将其应用于目标检测已成为当今计算机视觉领域关注的重点.对此,从支持向量机原理、目标特征模型构建、学习训练和目标检测框确定等角度,综述了基于支持向量机的目标检测算法的研究现状,并就进一步的发展进行了展望.

英文摘要:

The purpose of object detection is to detect and locate the object with a certain class from the static image or videos, and many studies simplify the object detection as a binary classification problem. For the reason that the support vector machine(SVM) can solve the pattern recognition problem well, especially the binary classification problem, how to use the SVM in computer vision becomes a hot point of many researchers. The status of object detection methods based on SVM is reviewed by introducing the concept and theory of SVM, the building of object feature model, training process and location of detection box. Finally, the future work of object detection methods based on SVM is discussed.

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