Fashion Object Detection for Tops & Bottoms
Andreas Petridis Mirela Popa Filipa Peleja Dario Dotti Alberto de Santos
Fashion is one of the largest world's industries and computer vision
techniques have been becoming more popular in recent years, in particular, for
tasks such as object detection and apparel segmentation. Even with the rapid
growth in computer vision solutions, specifically for the fashion industry,
many problems are far for being resolved. Therefore, not at all times,
adjusting out-of-the-box pre-trained computer vision models will provide the
desired solution. In the present paper is proposed a pipeline that takes a
noisy image with a person and specifically detects the regions with garments
that are bottoms or tops. Our solution implements models that are capable of
finding human parts in an image e.g. full-body vs half-body, or no human is
found. Then, other models knowing that there's a human and its composition
(e.g. not always we have a full-body) finds the bounding boxes/regions of the
image that very likely correspond to a bottom or a top. For the creation of
bounding boxes/regions task, a benchmark dataset was specifically prepared. The
results show that the Mask RCNN solution is robust, and generalized enough to
be used and scalable in unseen apparel/fashion data.