Decoding the fashion trend of sports shoes with empowered computer vision

Abstract

To adapt to the rapidly changing market and capricious trends, fashion brands need to understand trends and market conditions precisely and fast to produce marketable products. The traditional fashion trend analysis has relied heavily on the subjective judgement of experts, inevitably leading to biased decisions and is time-consuming. The development of computer vision and machine learning provides an objective and systematic approach to processing images and analysis of fashion products. However, most studies focus on clothing trends analysis, few are on shoe trends analysis. Hence, this study aimed to decode and analyse the fashion trend of sports shoes with empowered computer vision technology. In this paper, a dataset containing e-commerce images of sports shoes with precise annotations was established; then Mask-RCNN was utilized to classify and extract the shoe from the background image; a modified version of the K-means clustering algorithm was employed to detect the shoe colour. The results indicated that fashionable sports shoes and casual sports shoes were the most prevalent two styles. Besides neutral tones, yellow, red ochre and reddish orange were popular in casual sports shoes, fashion sports shoes and basketball shoes respectively and Atlantic Blue in board shoes and trainers. This study demonstrated the promising potential of computer vision and machine learning as a new method to analyse footwear fashion trends efficiently and economically.