图 书 馆
基于随机森林的多视点人体部位识别Multi-view Body Part Recognition with Random Forests |
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| 课程网址: | http://videolectures.net/bmvc2013_burenius_part_recognition/ |
| 主讲教师: | Magnus Burenius |
| 开课单位: | KTH-皇家理工学院 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  鉴于从多个动态但经过校准的相机拍摄的图像，本文解决了人体姿态估计的问题。我们考虑使用基于零件的模型来解决此任务，并将重点放在这种模型的零件外观组件上。我们使用随机森林分类器来捕获2D图像中身体部位外观的变化。然后，将这些2D零件检测器的结果汇总到各个视图中，以生成零件的一致3D假设。通过引入潜在变量，我们解决了镜像对称零件的视图间的对应关系。我们从职业足球比赛收集的数据集中定性和定量地评估零件检测器。 |
| 课程简介: | This paper addresses the problem of human pose estimation, given images taken from multiple dynamic but calibrated cameras. We consider solving this task using a part-based model and focus on the part appearance component of such a model. We use a random forest classifier to capture the variation in appearance of body parts in 2D images. The result of these 2D part detectors are then aggregated across views to produce consistent 3D hypotheses for parts. We solve correspondences across views for mirror symmetric parts by introducing a latent variable. We evaluate our part detectors qualitatively and quantitatively on a dataset gathered from a professional football game. |
| 关 键 词: | 零件检测; 潜在变量; 数据集中 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-25：zyk |
| 最后编审: | 2021-03-25：zyk |
| 阅读次数: | 48 |