Removing pedestrians from Google street view images

Since the introduction of Google Street View, a part of Google Maps, vehicles equipped with roof-mounted mobile cameras have methodically captured street-level images of entire cities. The worldwide Street View coverage spans over 10 countries in four different continents. This service is freely available to anyone with an internet connection. While this is seen as a valuable service, the images are taken in public spaces, so they also contain license plates, faces, and other information information deemed sensitive from a privacy standpoint. Privacy concerns have been expressed by many, in particular in European countries. As a result, Google has introduced a system that automatically blurs faces in Street View images. However, many identifiable features still remain on the un-blurred person. In this paper, we propose an automatic method to remove entire pedestrians from Street View images in urban scenes. The resulting holes are filled in with data from neighboring views. A compositing method for creating “ghost-free” mosaics is used to minimize the introduction of artifacts. This yields Street View images as if the pedestrians had never been there. We present promising results on a set of images from cities around the world.

[1]  Harry Shum,et al.  Construction and refinement of panoramic mosaics with global and local alignment , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[3]  James Davis,et al.  Mosaics of scenes with moving objects , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[4]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Jianhong Shen,et al.  Inpainting and the Fundamental Problem of Image Processing , 2002 .

[7]  SchieleBernt,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008 .

[8]  Christian Früh,et al.  An Automated Method for Large-Scale, Ground-Based City Model Acquisition , 2004, International Journal of Computer Vision.

[9]  Jan Boehm Multi-image fusion for occlusion-free faÇade texturing , 2004 .

[10]  Alexei A. Efros,et al.  Image quilting for texture synthesis and transfer , 2001, SIGGRAPH.

[11]  Richard Szeliski,et al.  Construction of Panoramic Image Mosaics with Global and Local Alignment , 2001 .

[12]  Marco Zennaro,et al.  Large-scale privacy protection in Google Street View , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  Ariel Shamir,et al.  Seam Carving for Content-Aware Image Resizing , 2007, ACM Trans. Graph..