  Nighttime surveillance suffers from degradation due to poor illumination and
arduous human annotations. It is challengable and remains a security risk at
night. Existing methods rely on multi-spectral images to perceive objects in
the dark, which are troubled by low resolution and color absence. We argue that
the ultimate solution for nighttime surveillance is night-to-day translation,
or Night2Day, which aims to translate a surveillance scene from nighttime to
the daytime while maintaining semantic consistency. To achieve this, this paper
presents a Disentangled Contrastive (DiCo) learning method. Specifically, to
address the poor and complex illumination in the nighttime scenes, we propose a
learnable physical prior, i.e., the color invariant, which provides a stable
perception of a highly dynamic night environment and can be incorporated into
the learning pipeline of neural networks. Targeting the surveillance scenes, we
develop a disentangled representation, which is an auxiliary pretext task that
separates surveillance scenes into the foreground and background with
contrastive learning. Such a strategy can extract the semantics without
supervision and boost our model to achieve instance-aware translation. Finally,
we incorporate all the modules above into generative adversarial networks and
achieve high-fidelity translation. This paper also contributes a new
surveillance dataset called NightSuR. It includes six scenes to support the
study on nighttime surveillance. This dataset collects nighttime images with
different properties of nighttime environments, such as flare and extreme
darkness. Extensive experiments demonstrate that our method outperforms
existing works significantly. The dataset and source code will be released on
GitHub soon.
