An intelligent image processing model for context-aware digital signage: case of apparel advertisement

Abstract

Digital Signage is a way of presenting content such as advertisements, news, menus, and directions on electronic displays on places with high human traffic, Stadiums, Transport hubs, Malls, Retail Stores, and Notice boards. People have a variety of preferences when it comes to style and design of apparel. In developing advertising content, people feel more engaged when viewing tailor-made content fashioned toward a set of characteristics such are skin tone, age, and gender. The ability of digital signs to detect its contextual surrounding and intelligently display an apparel advertisement has been on-demand to create an autonomous content generator for digital signage. Currently, digital advertising content is developed by a designer who has experience both in computer skills, digital design with creativity and innovation. Content management software preloaded onto the digital screens makes it simple to load and display content. However, small scale retailers who invest in digital signage fail to achieve its full potential due to limited knowledge in creating a context-aware advertisement. The proposed research applies machine learning technique to build a model which captures a potential customer’s apparel features using image processing then display a recommended outfit based on the input features. The result is a digital signage that autonomously creates a context-aware apparel advertisement based on what a potential customer is wearing. The model was evaluated based on accuracy, precision, and recall. Anthropometric measurements coupled with apparel segmentation fed into R-CNN gave the best apparel classification. The model’s yielded an accuracy of 94.39%, with a precision of 0.72 and 0.84 recall, respectively.