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购物失败!大型室内空间中的单眼定位

Lost Shopping! Monocular Localization in Large Indoor Spaces
课程网址: http://videolectures.net/iccv2015_wang_lost_shopping/  
主讲教师: Shenlong Wang
开课单位: 多伦多大学
开课时间: 2016-02-10
课程语种: 英语
中文简介:
在本文中,我们提出了一种在非常大的室内空间(即拥有200多家商店的购物中心)进行定位的新方法,该方法将单个图像和环境平面图作为输入。我们将定位问题表述为马尔可夫随机场中的推断,这共同导致了文本检测(在图像中定位具有精确边界框的商店名称)、商店门面分割以及整个购物中心内的相机旋转和平移。我们方法的强大之处在于,它不使用任何关于外观的先验信息,而是利用与商店名称相对应的文本检测作为本地化提示。这使得我们的方法适用于各种领域,并且能够存储不同国家、季节和照明条件下的外观变化。我们在两个大型购物中心的新数据集上演示了我们的方法,并展示了整体推理的能力。
课程简介: In this paper we propose a novel approach to localization in very large indoor spaces (i.e., shopping malls with over 200 stores) that takes a single image and a floor plan of the environment as input. We formulate the localization problem as inference in a Markov random field, which jointly reasons about text detection (localizing shop names in the image with precise bounding boxes), shop facade segmentation, as well as camera’s rotation and translation within the entire shopping mall. The power of our approach is that it does not use any prior information about appearance and instead exploits text detections corresponding to shop names as a cue for localization. This makes our method applicable to a variety of domains and robust to store appearance variation across countries, seasons, and illumination conditions. We demonstrate our approach on our new dataset spanning two very large shopping malls, and show the power of holistic reasoning.
关 键 词: 室内空间; 环境平面图; 新数据集
课程来源: 视频讲座网
数据采集: 2022-12-07:chenjy
最后编审: 2022-12-07:chenjy
阅读次数: 16