Hierarchical Paired Channel Fusion Network for Street Scene Change Detection

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Hierarchical Paired Channel Fusion Network for Street Scene Change Detection

By: 
Yinjie Lei, Duo Peng, Pingping Zhang, Qiuhong Ke, Haifeng Li

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.

SPS on Twitter

  • The 2021 IEEE International Symposium on Biomedical Imaging virtual platform is live, featuring pre-recorded talks… https://t.co/JfRAvO5hqr
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special Iss… https://t.co/fQ25UHWidg
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is now accepting submissions for a Spec… https://t.co/AuMC67sUKd
  • The SPACE Webinar Series continues Tuesday, 6 April at 10:00 AM EDT when Dr. Ivan Dokmanić presents "Learning the G… https://t.co/4coVRWm0lc
  • NEW SPS WEBINAR: Join us on Wednesday, 28 April at 1:00 PM EDT when Dr. Fernando Gama presents "Graph Neural Networ… https://t.co/UI6Oi2PYYi

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar