Illumination Distillation Framework for Nighttime Person
  Re-Identification and A New Benchmark
Andong Lu Zhang Zhang Yan Huang Yifan Zhang Chenglong Li Jin Tang Liang Wang
Nighttime person Re-ID (person re-identification in the nighttime) is a very
important and challenging task for visual surveillance but it has not been
thoroughly investigated. Under the low illumination condition, the performance
of person Re-ID methods usually sharply deteriorates. To address the low
illumination challenge in nighttime person Re-ID, this paper proposes an
Illumination Distillation Framework (IDF), which utilizes illumination
enhancement and illumination distillation schemes to promote the learning of
Re-ID models. Specifically, IDF consists of a master branch, an illumination
enhancement branch, and an illumination distillation module. The master branch
is used to extract the features from a nighttime image. The illumination
enhancement branch first estimates an enhanced image from the nighttime image
using a nonlinear curve mapping method and then extracts the enhanced features.
However, nighttime and enhanced features usually contain data noise due to
unstable lighting conditions and enhancement failures. To fully exploit the
complementary benefits of nighttime and enhanced features while suppressing
data noise, we propose an illumination distillation module. In particular, the
illumination distillation module fuses the features from two branches through a
bottleneck fusion model and then uses the fused features to guide the learning
of both branches in a distillation manner. In addition, we build a real-world
nighttime person Re-ID dataset, named Night600, which contains 600 identities
captured from different viewpoints and nighttime illumination conditions under
complex outdoor environments. Experimental results demonstrate that our IDF can
achieve state-of-the-art performance on two nighttime person Re-ID datasets
(i.e., Night600 and Knight ). We will release our code and dataset at
https://github.com/Alexadlu/IDF.