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Object Tracking with Multiple Instance Learning and Gaussian Mixture Model

机译：具有多实例学习和高斯混合模型的对象跟踪

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摘要

Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes.
机译：最近，针对对象跟踪应用引入了多实例学习（MIL）技术，该技术已显示出处理漂移问题的良好性能。尽管正面袋中的某些实例不仅包含对象，而且还包含背景，但仅假设正面袋中的实例的每个特征服从单个高斯分布是不可靠的。本文开发了一种基于在线多实例增强的跟踪器，该跟踪器分别采用高斯混合模型（GMM）和单高斯分布来对正袋和负袋中的实例特征进行建模。样本和模型之间的差异被整合到GMM参数更新过程中。从袋子中提取类似Haar的特征，训练一组弱分类器以构造一个强分类器，该分类器用于在新帧上跟踪对象的位置。分类器可以逐帧在线更新。实验结果表明，我们的跟踪器在处理照明，旋转，姿势和外观变化时更加稳定和高效。

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  • 来源
    《Journal of information and computational science》 |2015年第11期|4465-4477|共13页
  • 作者

    Na Li; Xiangmo Zhao; Daxiang Li; Jing Wang; Bendu Bai;

  • 作者单位

    School of Information Engineering, Chang'an University, Xi'an 710064, China,School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China;

    School of Information Engineering, Chang'an University, Xi'an 710064, China;

    School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China;

    School of Computing and Engineering University of Huddersfield, Huddersfield HD1 3DH United Kingdom;

    School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Object Tracking; Multiple Instance Learning; Gaussian Mixture Model;

    机译：对象跟踪;多实例学习;高斯混合模型;

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