The World Is Changing: Finding Changes on the Street

  • Kuan-Ting Chen
  • Fu-En Wang
  • Juan-Ting Lin
  • Fu-Hsiang Chan
  • Min SunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)


We propose to find changes in the constantly changing world, given visual observations at street-level. In particular, we identify “long-term” changes between Google Street View images and dashcam videos captured at different months or even years. This is a challenging task, since (1) dashcam frames are not localized in world coordinate, and (2) there are many changes introduced by moving objects. We propose a robust sequence alignment method to align dashcam sequence to Street View images. Our method outperforms a strong baseline method [1] by \(12\%\) mean Average Precision (AP). We also propose a novel change detection method designed to detect long-term changes. Our change detection method (\({13.54}\%\)) outperforms a baseline method without handling car interior and moving objects (\({11.70}\%\)) by \(1.84\%\) (relatively \(13.6\%\)) in mean AP. In a controlled experiment, given manually aligned high quality Street View images, our change detection method achieves a significantly better mean AP (\(45.57\%\)).


Reference Image Average Precision Reconstruction Error Query Image Confidence Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Industrial Technology Research Institute (ITRI) project grants and MOST 103-2218-E-007-025 and MOST 104-3115-E-007-005 in Taiwan for their support.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kuan-Ting Chen
    • 1
  • Fu-En Wang
    • 1
  • Juan-Ting Lin
    • 1
  • Fu-Hsiang Chan
    • 1
  • Min Sun
    • 1
    Email author
  1. 1.Department of Electrical EngineeringNational Tsing Hua UniversityHsinchuTaiwan

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