ABSTRACT
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.
ABSTRACT
The inability to prevent or eliminate illicit trafficking of cultural goods is not limited to failed-state environments or any specific part of the globe. While the antiquities market denies that this illicit trade is a widespread phenomenon, the international community and Law Enforcement Agencies (LEAs) overwhelmingly recognize the problem indicating that organized crime is involved at all stages. Nowadays, web platforms play host to groups dedicated to illegal archaeological excavations and Illicit trade of cultural goods. Looters have the freedom to connect online with potential buyers around the world. At the same time, social media platform monitoring in search of criminal activities conducted by LEAs is poor due to the lack of expertise, efficient tools to scan the massive amounts of data, and funds. The COVID-19 crisis has compounded the problem by driving more and more dealers and buyers online – where they are discovering that by joining certain unmonitored groups, they can enter the illegal market with ease. The EU funded SIGNIFICANCE project (Stop Illicit heritaGe traffickiNg wIth artiFICiAl iNtelligenCE) has been designed to boost LEAs investigation capabilities in monitoring online illegal activities on social media platforms, the web and the dark web for the identification of cultural property crimes, exploiting Artificial Intelligence and Deep Learning algorithms for guaranteeing the successful prosecution of perpetrators unveiling criminal networks.
ABSTRACT
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.