Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects
Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects
复制标题
用于多视图多姿势物体检测的基于样本的学习分类
DOI：
10.1109/cvpr.2006.168
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Rakesh Kumar
中科院分区：
文献类型：
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作者：
Ying Shan;Feng Han;H. Sawhney;Rakesh Kumar
提出了一种新的基于形状样本的多视角多姿态目标检测方法。该方法的核心思想是基于大量以前的观察结果，将多视点多姿态训练数据手动聚类到不同的类别，然后将单独训练的两类分类器组合在一起，大大提高了检测性能。提出了一种新的计算框架来统一不同的分类过程，为每个类内类别训练个体分类器，并训练结合个体分类器的强分类器。各个进程使用单个目标函数，该函数使用两个嵌套的AdaBoost循环进行优化。外部的AdaBoost循环用于选择区分样本，而内部的AdaBoost循环用于选择所选样本上的区分特征。提出的方法取代了人工选择样本的耗时过程，并解决了这一过程中固有的标注歧义问题。此外，在实时实现方面，我们的方法完全符合基于AdaBoost的标准目标检测框架。在多视角、多姿态人和车辆数据上的实验证明了该方法的有效性。
This paper proposes a novel approach for multi-view multi-pose object detection using discriminative shapebased exemplars. The key idea underlying this method is motivated by numerous previous observations that manually clustering multi-view multi-pose training data into different categories and then combining the separately trained two-class classifiers greatly improved the detection performance. A novel computational framework is proposed to unify different processes of categorization, training individual classifier for each intra-class category, and training a strong classifier combining the individual classifiers. The individual processes employ a single objective function that is optimized using two nested AdaBoost loops. The outer AdaBoost loop is used to select discriminative exemplars and the inner AdaBoost is used to select discriminative features on the selected exemplars. The proposed approach replaces the manual time-consuming process of exemplar selection as well as addresses the problem of labeling ambiguity inherent in this process. Also, our approach fully complies with the standard AdaBoost-based object detection framework in terms of real-time implementation. Experiments on multi-view multi-pose people and vehicle data demonstrate the efficacy of the proposed approach.