Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing
Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing
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DOI：
10.1109/cvpr.2007.383498
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
V. Sharma;James W. Davis
中科院分区：
文献类型：
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作者：
V. Sharma;James W. Davis
我们提出了一种从静态图像中同时检测和分割人的方法。该技术在训练过程中不需要人工分割，并且在单一框架内利用自顶向下和自底向上的处理来进行物体定位和二维形状估计。首先，利用低级边缘特征从简单的训练阶段学习物体的粗形状。由于观察到大多数对象类别具有规则的形状和封闭的边界，这些特征之间的关系随后被利用来获得中级线索，例如连续性和封闭性。提出了一种基于边缘特征的马尔可夫随机场，该随机场将粗糙的形状信息与我们期望的物体可能具有规则和封闭的边界相结合。该算法在不同难度的行人数据集上进行评估，包括大范围的摄像机视点和人的方向。给出了人的检测和分割的定量结果，证明了所提出的技术同时解决这两个任务的有效性。
We present a method for the simultaneous detection and segmentation of people from static images. The proposed technique requires no manual segmentation during training, and exploits top-down and bottom-up processing within a single framework for both object localization and 2D shape estimation. First, the coarse shape of the object is learned from a simple training phase utilizing low-level edge features. Motivated by the observation that most object categories have regular shapes and closed boundaries, relations between these features are then exploited to derive mid-level cues, such as continuity and closure. A novel Markov random field defined on the edge features is presented that integrates the coarse shape information with our expectation that objects are likely to have boundaries that are regular and closed. The algorithm is evaluated on pedestrian datasets of varying difficulty, including a wide range of camera viewpoints, and person orientations. Quantitative results are presented for person detection and segmentation, demonstrating the effectiveness of the proposed technique to simultaneously address both these tasks.