The analysis of daily-life fashion trends can provide us a profound understanding of our societies and cultures. However, no appropriate digital archive exists that includes images illustrating what people wore in their daily lives over an extended period. In this study, we propose a new fashion image archive, Chronicle Archive of Tokyo Street-fashion (CAT STREET), to shed light on daily-life fashion trends. CAT STREET includes images showing what people wore in their daily lives during 1970--2017, and these images contain timestamps and street location annotations. This novel database combined with machine learning enables us to observe daily-life fashion trends over a long term and analyze them quantitatively. To evaluate the potential of our proposed approach with the novel database, we corroborated the rules-of-thumb of two fashion trend phenomena that have been observed and discussed qualitatively in previous studies. Through these empirical analyses, we verified that our approach to quantify fashion trends can help in exploring unsolved research questions. We also demonstrate CAT STREET's potential to find new standpoints to promote the understanding of societies and cultures through fashion embedded in consumers' daily lives.
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