  Change detection has been a challenging visual task due to the dynamic nature
of real-world scenes. Good performance of existing methods depends largely on
prior background images or a long-term observation. These methods, however,
suffer severe degradation when they are applied to detection of instantaneously
occurred changes with only a few preceding frames provided. In this paper, we
exploit spatio-temporal convolutional networks to address this challenge, and
propose a novel retrospective convolution, which features efficient change
information extraction between the current frame and frames from historical
observation. To address the problem of foreground-specific over-fitting in
learning-based methods, we further propose a data augmentation method, named
static sample synthesis, to guide the network to focus on learning change-cued
information rather than specific spatial features of foreground. Trained
end-to-end with complex scenarios, our framework proves to be accurate in
detecting instantaneous changes and robust in combating diverse noises.
Extensive experiments demonstrate that our proposed method significantly
outperforms existing methods.
