Example-based object detection in images by components
Example-based object detection in images by components
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DOI：
10.1109/34.917571
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发表时间：
2001-04-01
影响因子：
23.6
通讯作者：
Poggio, T
中科院分区：
文献类型：
--
作者：
Mohan, A;Papageorgiou, C;Poggio, T
在本文中，我们提出了一个通用的基于实例的框架，通过组件来检测静态图像中的目标。这项技术是通过开发一个在混乱场景中定位人的系统来演示的。该系统由四个不同的基于实例的探测器组成，这些探测器经过训练，分别找到人体的四个组成部分：头部。腿、左臂和右臂。在确保这些组件以正确的几何配置存在之后，第二个基于实例的分类器组合组件检测器的结果以将模式分类为“人”或“非人”。我们将这种在多个阶段进行学习的分层体系结构称为自适应分类器组合(ACC)。我们给出的结果表明，该系统的性能明显好于类似的全身人检测器。这表明，性能的提高归功于基于组件的方法和ACC数据分类体系结构。该算法也比全身人检测方法更健壮，因为它能够定位部分遮挡的人和身体部位与背景对比度较小的人。
In this paper, we present a general example-based framework for detecting objects in static images by components. The technique is demonstrated by developing a system that locates people in cluttered scenes. The system is structured with four distinct example-based detectors that are trained to separately find the four components of the human body: the head. legs, left arm, and right arm. After ensuring that these components are present in the proper geometric configuration, a second example-based classifier combines the results of the component detectors to classify a pattern as either a "person" or a "nonperson." We call this type of hierarchical architecture, in which learning occurs at multiple stages, an Adaptive Combination of Classifiers (ACC). We present results that show that this system performs significantly better than a similar full-body person detector. This suggests that the improvement in performance is due to the component-based approach and the ACC data classification architecture. The algorithm is also more robust than the full-body person detection method in that it is capable of locating partially occluded Views of people and people whose body parts have little contrast with the background.