USC-GRAD-STDdb

By Brais Bosquet, Manuel Mucientes, Víctor Manuel Brea at CiTIUS and David de la Iglesia, Raquel Dosil, Daniel González at Gradiant.

Introduction

The Small Target Detection database (USC-GRAD-STDdb) is a set of annotated video segments retrieved from YouTube. USC-GRAD-STDdb is holded by Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS) of the University of Santiago de Compostela (USC), Spain and by Centro Tecnológico de Telecomunicaciones de Galicia (Gradiant), Spain.

To the best of our knowledge, USC-GRAD-STDdb is the first database that has enough amount of small objects (objects smaller than ≈16x16 pixels) to train and test small object detection frameworks.

Database description

USC-GRAD-STDdb comprises 115 video segments containing more than 25,000 annotated frames of HD 720p resolution (≈1280x720) with small objects of interest from 16 (≈4x4) to 256 (≈16x16) as pixel area. The length of the videos changes from 150 up to 500 frames. The size of every object is determined through the bounding box, so that a good annotation is of utmost importance for reliable performance metrics. As it may seem obvious, the smaller the object, the harder the annotation. The annotation has been carried out with the ViTBAT tool, adjusting the boxes as much as possible to the objects of interest in each video frame. In total, more than 56,000 ground truth labels have been generated.