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A critical challenge problem of scene change detection is that noisy changesgenerated by varying illumination, shadows and camera viewpoint make variancesof a scene difficult to define and measure since the noisy changes and semanticones are entangled. Following the intuitive idea of detecting changes bydirectly comparing dissimilarities between a pair of features, we propose anovel fully Convolutional siamese metric Network(CosimNet) to measure changesby customizing implicit metrics. To learn more discriminative metrics, weutilize contrastive loss to reduce the distance between the unchanged featurepairs and to enlarge the distance between the changed feature pairs.Specifically, to address the issue of large viewpoint differences, we proposeThresholded Contrastive Loss (TCL) with a more tolerant strategy to punishnoisy changes. We demonstrate the effectiveness of the proposed approach withexperiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD. Ourapproach is robust to lots of challenging conditions, such as illuminationchanges, large viewpoint difference caused by camera motion and zooming. Inaddition, we incorporate the distance metric into the segmentation frameworkand validate the effectiveness through visualization of change maps and featuredistribution. The source code is available athttps://github.com/gmayday1997/ChangeDet.

Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection