水准点(测量)
计算机科学
任务(项目管理)
人工智能
图像(数学)
基本事实
比例(比率)
RGB颜色模型
计算机视觉
班级(哲学)
数据挖掘
地理
地图学
工程类
系统工程
作者
Emiliano Spera,Antonino Furnari,Sebastiano Battiato,Giovanni Maria Farinella
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-04-01
卷期号:31 (4): 1253-1267
被引量:11
标识
DOI:10.1109/tcsvt.2019.2941040
摘要
We consider the task of localizing shopping carts in a retail store from egocentric images. Addressing this task allows to infer information on the behavior of the customers to understand how they move in the store and what they pay more attention to. To study the problem, we propose a large dataset of images collected in a real retail store. The dataset comprises 19, 531 RGB images along with depth maps, ground truth camera poses, as well as class labels specifying the areas of the store in which each image has been acquired. We release the dataset to the public to encourage research in large-scale image-based indoor localization and to address the scarcity of large datasets to tackle the problem. We hence perform a benchmark of several image-based localization techniques exploiting images and depth information on the proposed dataset. In our study, both localization performances and space/time requirements are compared. The results show that, while state-of-the-art approaches allow to achieve good results, there is space for improvement.
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