Hierarchical Paired Channel Fusion Network for Street Scene Change Detection

TL;DR

This paper introduces HPCFNet, a hierarchical neural network that adaptively fuses features at multiple levels for more accurate street scene change detection, effectively handling diverse change scales and locations.

Contribution

The paper proposes a novel hierarchical paired channel fusion network with a multi-part feature learning strategy for improved street scene change detection.

Findings

Outperforms state-of-the-art methods on three public datasets.

Effectively handles diverse change scales and locations.

Achieves superior accuracy in change map generation.

Abstract

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.