Street Scene Change Detection (SSCD) aims to locate the changed regions
between a given street-view image pair captured at different times, which is an
important yet challenging task in the computer vision community. The intuitive
way to solve the SSCD task is to fuse the extracted image feature pairs, and
then directly measure the dissimilarity parts for producing a change map.
Therefore, the key for the SSCD task is to design an effective feature fusion
method that can improve the accuracy of the corresponding change maps. To this
end, we present a novel Hierarchical Paired Channel Fusion Network (HPCFNet),
which utilizes the adaptive fusion of paired feature channels. Specifically,
the features of a given image pair are jointly extracted by a Siamese
Convolutional Neural Network (SCNN) and hierarchically combined by exploring
the fusion of channel pairs at multiple feature levels. In addition, based on
the observation that the distribution of scene changes is diverse, we further
propose a Multi-Part Feature Learning (MPFL) strategy to detect diverse
changes. Based on the MPFL strategy, our framework achieves a novel approach to
adapt to the scale and location diversities of the scene change regions.
Extensive experiments on three public datasets (i.e., PCD, VL-CMU-CD and
CDnet2014) demonstrate that the proposed framework achieves superior
performance which outperforms other state-of-the-art methods with a
considerable margin.

提出了一种基于多层级特征融合和多部分特征学习的 Hierarchical Paired Channel Fusion Network (SSCD) 框架，用于改善街景图像对中场景变化检测的精度，实验结果表明该方法在三个公共数据集上均优于其他现有方法。