Small Hand-held Object Recognition Test (SHORT)

Abstract

The ubiquity of smartphones with high quality cameras and fast network connections will spawn many new applications. One of these is visual object recognition, an emerging smartphone feature which could play roles in high-street shopping, price comparisons and similar uses. There are also potential roles for such technology in assistive applications, such as for people who have visual impairment. We introduce the Small Hand-held Object Recognition Test (SHORT), a new dataset that aims to benchmark the performance of algorithms for recognising hand-held objects from either snapshots or videos acquired using hand-held or wearable cameras. We show that SHORT provides a set of images and ground truth that help assess the many factors that affect recognition performance. SHORT is designed to be focused on the assistive systems context, though it can provide useful information on more general aspects of recognition performance for hand-held objects. We describe the present state of the dataset, comprised of a small set of high quality training images and a large set of nearly 135,000 smartphone-captured test images of 30 grocery products. In this version, SHORT addresses another context not covered by traditional datasets, in which high quality catalogue images are being compared with variable quality user-captured images; this makes the matching more challenging in SHORT than other datasets. Images of similar quality are often not present in “database” and “query” datasets, a situation being increasingly encountered in commercial applications. Finally, we compare the results of popular object recognition algorithms of different levels of complexity when tested against SHORT and discuss the research challenges arising from the particularities of visual object recognition from objects that are being held by users.