High F-score Model for Recognizing Object Visibility in Images with Occluded Objects of Interest
Bernardas Čiapas, Povilas Treygis · Baltic Journal of Modern Computing · 2021
Article investigates recognition of partially occluded objects of interest.Images from retail store self checkout area often contain products that are covered by a customer's body parts, are placed inside semi-transparent plastic bags, include intensive glare, or some combination of these.In order to categorize objects of interest in images with partially occluded objects, the first step is to decide if an image contains enough information about the object of interest in order to be categorized.The most famous computer vision data sets -such as Imagenet, Canadian Institute for Advanced Research (CIFAR), Digits by National Institute of Standards and Technology (MNIST) -are made of images that contain clearly visible, distinctive objects of interest and are only labelled with binary information about object existence; reduced visibility objects are absent in the mentioned datasets.Such binary visibility labels are not suitable for solving the recognition task of object occlusion level.In this study authors categorize images into [not] containing enough information about objects of interest in order to be categorized.Authors analyze a dataset collected in a real retail store self checkout area where objects of interest are various products.The proposed method uses 6 categories of occlusion variously grouped.Authors received >0.9 F-score in best model separating images into object visible/invisible categories.