preprints.org > mathematics & computer science > algebra & number theory > doi: 10.20944/preprints202012.0403.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 14 December 2020 / Approved: 16 December 2020 / Online: 16 December 2020 (10:37:47 CET)

Computer vision is considered as an ally to solve business problems that require human intervention, intelligence and criteria. This topic of research has evolved in XXI century at faster peace, delivering various alternatives from open source until commercial platforms. With so many options and market growing, it result difficult to make a decision on which one to use, or even worse, realize it was not suited for different scenarios. In this paper we analyze five options selected arbitrarily and tested on a dataset of 755 images to detect persons in an image, using object detectors. We analyze elapsed time to process an image, error with observations by humans, number of persons detected, correlation of time and person density, object detected size and F1 Score, considering precision and recall. As we found there are score ties and similar behaviors among options available, we introduce a novel index that takes in consideration the number of persons and their pixel size, to propose the Vision Acuity Index of Computer Vision. The results demonstrate this is a good option to serve as indicator to make decisions. Also, this index proposed have a potential to be expanded for different business use cases, and to measure new proposed algorithms in the future along with the traditional metrics used previously.

Computer vision; performance metrics; Yolo3; AWS Rekognition; Azure Computer Vision

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory