Long-term Person Re-identification: A Benchmark
- URL: http://arxiv.org/abs/2105.14685v1
- Date: Mon, 31 May 2021 03:35:00 GMT
- Title: Long-term Person Re-identification: A Benchmark
- Authors: Peng Xu and Xiatian Zhu
- Abstract summary: In realworld we often dress ourselves differently across locations, time, dates, seasons, weather, and events.
This work contributes timely a large, realistic long-term person re-identification benchmark.
It consists of 171K bounding boxes from 1.1K person identities, collected and constructed over a course of 12 months.
- Score: 57.97182942537195
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing person re-identification (Re-ID) works mostly consider a short-term
search problem assuming unchanged clothes and personal appearance. However, in
realworld we often dress ourselves differently across locations, time, dates,
seasons, weather, and events. As a result, the existing methods are unsuitable
for long-term person Re-ID with clothes change involved. Whilst there are
several recent longterm Re-ID attempts, a large realistic dataset with clothes
change is lacking and indispensable for enabling extensive study as already
experienced in short-term Re-ID setting. In this work, we contribute timely a
large, realistic long-term person re-identification benchmark. It consists of
171K bounding boxes from 1.1K person identities, collected and constructed over
a course of 12 months. Unique characteristics of this dataset include: (1)
Natural/native personal appearance (e.g., clothes and hair style) variations:
The degrees of clothes-change and dressing styles all are highly diverse, with
the reappearing gap in time ranging from minutes, hours, and days to weeks,
months, seasons, and years. (2) Diverse walks of life: Persons across a wide
range of ages and professions appear in different weather conditions (e.g.,
sunny, cloudy, windy, rainy, snowy, extremely cold) and events (e.g., working,
leisure, daily activities). (3) Rich camera setups: The raw videos were
recorded by 17 outdoor security cameras with various resolutions operating in a
real-world surveillance system for a wide and dense block. (4) Largest scale:
It covers the largest number of (17) cameras, (1082) identities, and (171K)
bounding boxes, as compared to alternative datasets.
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