  Existing person re-identification (re-id) works mostly consider short-term
application scenarios without clothes change. In real-world, however, we often
dress differently across space and time. To solve this contrast, a few recent
attempts have been made on long-term re-id with clothes change. Currently, one
of the most significant limitations in this field is the lack of a large
realistic benchmark. In this work, we contribute a large, realistic long-term
person re-identification benchmark, named as DeepChange. It has several unique
characteristics: (1) Realistic and rich personal appearance (e.g., clothes and
hair style) and variations: Highly diverse clothes change and styles, with
varying reappearing gaps in time from minutes to seasons, different weather
conditions (e.g., sunny, cloudy, windy, rainy, snowy, extremely cold) and
events (e.g., working, leisure, daily activities). (2) Rich camera setups: Raw
videos were recorded by 17 outdoor varying resolution cameras operating in a
real-world surveillance system. (3) The currently largest number of (17)
cameras, (1, 121) identities, and (178, 407) bounding boxes, over the longest
time span (12 months). Further, we investigate multimodal fusion strategies for
tackling the clothes change challenge. Extensive experiments show that our
fusion models outperform a wide variety of state-of-the-art models on
DeepChange. Our dataset and documents are available at
https://github.com/PengBoXiangShang/deepchange.
