手持物体识别的数据集

Jose Rivera-Rubio, Saad Idrees, I. Alexiou, Lucas Hadjilucas, A. Bharath
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引用次数: 6

摘要

视觉对象识别只是配备摄像头的智能手机的众多应用之一。通过一些大型互联网搜索提供商,通过可穿戴和手持相机拍摄的照片识别物体的能力已经成为可能;然而,很少有对搜索结果质量的严格分析,特别是在图像质量存在巨大差异的情况下。这促使我们开发小型手持对象识别测试(SHORT)。这包括一个适合从使用手持或可穿戴相机获取的快照或视频中识别手持物体的数据集。SHORT提供了一组图像和ground truth,帮助评估影响识别性能的不同因素。在目前的状态下,该数据集由一组高质量的训练图像和一组近13.5万张智能手机捕获的30种杂货产品的测试图像组成。在本文中,我们将讨论在用户持有的对象的视觉对象识别方面的一些开放挑战。我们评估了一些流行的对象识别算法的性能,具有不同的复杂程度,当测试SHORT时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dataset for Hand-Held Object Recognition
Visual object recognition is just one of the many applications of camera-equipped smartphones. The ability to recognise objects through photos taken with wearable and handheld cameras is already possible through some of the larger internet search providers; yet, there is little rigorous analysis of the quality of search results, particularly where there is great disparity in image quality. This has motivated us to develop the Small Hand-held Object Recognition Test (SHORT). This includes a dataset that is suitable for recognising hand-held objects from either snapshots or videos acquired using hand-held or wearable cameras. SHORT provides a collection of images and ground truth that help evaluate the different factors that affect recognition performance. At its present state, the dataset is comprised of a set of high quality training images and a large set of nearly 135,000 smartphone-captured test images of 30 grocery products. In this paper, we will discuss some open challenges in the visual object recognition of objects that are being held by users. We evaluate the performance of a number of popular object recognition algorithms, with differing levels of complexity, when tested against SHORT.
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