StyleTrendGAN: A Deep Learning Generative Framework for Fashion Bag Generation

Dealing with fashion multimedia big data with Artificial Intelligence (AI) algorithms has become an appealing challenge for computer scientists, since it can serve as inspiration for fashion designers and can also allow to predict the next trendy items in the fashion industry. Moreover, with the global spread of COVID-19 pandemic, social media contents have achieved an increasingly crucial factor in driving retail purchase decisions, thus it has become mandatory for fashion brand analysing social media pictures. In this light, this paper aims at presenting StyleTrendGAN, a novel custom deep learning framework that has the ability to generate fashion items. StyleTrendGAN combines a Dense Extreme Inception Network (DexiNed) for sketches extraction and Pix2Pix for the transformation of the input sketches into the new handbag models. StyleTrendGAN increases the efficiency and accuracy of the creation of new fashion models compared to previous ones and to the classic human approach;it aims to stimulate the creativity of designers and the visualization of the results of a production process without actually putting it into practice. The approach was applied and tested on a newly collected dataset, “MADAME” (iMage fAshion Dataset sociAl MEdia) of images collected from Instagram. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

People counting on low cost embedded hardware during the sars-cov-2 pandemic

Detecting and tracking people is a challenging task in a persistent crowded environment as retail, airport or station, for human behaviour analysis of security purposes. Especially during the global spread of SARS-CoV-2 virus that has become part of everyday life in every country, it is important to be able to manage the flows inside and outside buildings indoors. This article introduces an approach to detect and count people when they cross a virtual line. The methods used are based on deep learning and in particular on convolutional neural networks, specifically MobileNetV3 which is used for the detection task and MOSSE filter which is used for the tracking phase. The hardware system assembled for people counting is inexpensive, as it is formed by Raspberry Pi4 and a Picamera module v2. These devices have already been installed in some supermarkets and museums in the center of Italy, precisely in the area of the Marche region. © Springer Nature Switzerland AG 2021.