  A critical challenge problem of scene change detection is that noisy changes
generated by varying illumination, shadows and camera viewpoint make variances
of a scene difficult to define and measure since the noisy changes and semantic
ones are entangled. Following the intuitive idea of detecting changes by
directly comparing dissimilarities between a pair of features, we propose a
novel fully Convolutional siamese metric Network(CosimNet) to measure changes
by customizing implicit metrics. To learn more discriminative metrics, we
utilize contrastive loss to reduce the distance between the unchanged feature
pairs and to enlarge the distance between the changed feature pairs.
Specifically, to address the issue of large viewpoint differences, we propose
Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish
noisy changes. We demonstrate the effectiveness of the proposed approach with
experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Our
approach is robust to lots of challenging conditions, such as illumination
changes, large viewpoint difference caused by camera motion and zooming. In
addition, we incorporate the distance metric into the segmentation framework
and validate the effectiveness through visualization of change maps and feature
distribution. The source code is available at
https://github.com/gmayday1997/ChangeDet.
