Egocentric Shopping Cart Localization
Egocentric Shopping Cart Localization
复制标题
以自我为中心的购物车本地化
DOI：
10.1109/icpr.2018.8545516
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
G. Farinella
中科院分区：
文献类型：
--
作者：
Emiliano Spera;Antonino Furnari;S. Battiato;G. Farinella
这项工作研究了新的问题，基于图像的自我中心的购物车定位在零售商店。我们工作的贡献是双重的。首先，我们提出了一个新的大规模数据集的图像为基础的自我中心的购物车定位。该数据集是使用放置在大型零售店购物车上的相机收集的。它总共包含19，531个图像帧，每个帧都标有六个自由度姿势。我们通过分析如何表示和估计推车位置以及如何评估定位结果来研究定位问题。其次，我们对不同的基于图像的技术进行基准测试来解决这个问题。具体来说，我们研究了两个家庭的算法：经典的方法基于图像检索和新兴的方法基于回归。实验结果表明，基于图像检索的方法大大优于基于回归的方法。我们还指出，深度度量学习技术允许学习更好的视觉表示w.r.t.其他体系结构，并且对于改进基于检索和基于回归的方法的定位结果是有用的。我们的研究结果表明，深度度量学习技术可以帮助弥合基于检索和基于回归的方法之间的差距。
This work investigates the new problem of image-based egocentric shopping cart localization in retail stores. The contribution of our work is two-fold. First, we propose a novel large-scale dataset for image-based egocentric shopping cart localization. The dataset has been collected using cameras placed on shopping carts in a large retail store. It contains a total of 19,531 image frames, each labelled with its six Degrees Of Freedom pose. We study the localization problem by analysing how cart locations should be represented and estimated, and how to assess the localization results. Second, we benchmark different image-based techniques to address the task. Specifically, we investigate two families of algorithms: classic methods based on image retrieval and emerging methods based on regression. Experimental results show that methods based on image retrieval largely outperform regression-based approaches. We also point out that deep metric learning techniques allow to learn better visual representations w.r.t. other architectures, and are useful to improve the localization results of both retrieval-based and regression-based approaches. Our findings suggest that deep metric learning techniques can help bridge the gap between retrieval-based and regression-based methods.