Evaluation of sampling-based pedestrian detection for crowd counting
Evaluation of sampling-based pedestrian detection for crowd counting
复制标题
用于人群计数的基于采样的行人检测的评估
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
R. Collins
中科院分区：
文献类型：
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作者：
Weina Ge;R. Collins
随着计算能力的提高，曾经计算昂贵的基于采样的方法已经成功地应用于解决许多组合性质的硬视觉问题，如图像分割，视频跟踪和目标检测。在本文中，我们使用可逆跳马尔可夫链蒙特卡罗（RJMCMC）采样方法进行行人检测。人群场景被视为标记点过程（MPP）的实现，该过程由有界区域中的随机人群组成。每个人都与一个随机的“标记”相关联，该标记控制他们在图像中的位置和大小。为了自动推断场景中的人数及其空间位置，RJMCMC用于从潜在的随机过程中采样人员假设，并根据图像观察对其进行评估，以找到最佳解释图像的最佳配置。我们进一步扩展的检测器，假设人不在图像平面，但在3D空间，通过将多视图信息。在PETS 2009数据集中的人群计数任务上评估了单视图和多视图版本的检测性能。
With increases in computing power, the once computationally expensive sampling-based methods have been successfully applied to solve many hard vision problems of combinatorial nature, such as image segmentation, video tracking, and object detection. In this paper, we perform pedestrian detection using the reversible jump Markov Chain Monte Carlo (RJMCMC) sampling method. A crowd scene is viewed as a realization of a Marked Point Process (MPP) that consists of a random set of people in a bounded region. Each person is associated with a random ‘mark’ that governs their location and size in the image. To automatically infer the number of people in the scene and their spatial locations, RJMCMC is used to sample person hypotheses from an underlying stochastic process and evaluate them against the image observation to find the optimal configuration that best explains the image. We further extend the detector to hypothesize people not in the image plane but in 3D space, by incorporating multi-view information. The detection performance of both the single- and multi-view versions are evaluated on the crowd counting task in the PETS 2009 dataset.