FOSS4G 2017

Visualization and analysis of active transportation patterns derived from public webcams

Anna Petrasova, J. Aaron Hipp, Helena Mitasova
North Carolina State University

Public webcams

Rich source of spatio-temporal information
  • weather, traffic, changes in environment, phenology, ...
  • active transportation behavior in urban areas


The Archive of Many Outdoor Scenes
  • collection of long-term timelapse imagery from publicly accessible outdoor webcams around the world
  • 1,128,087,180 images taken from 29945 webcams
  • a project of the Media and Machines Lab Washington University in St. Louis
  • online browsing of images and download available
  • metadata and tags to improve discoverability of webcams

From image to information

How to get from image to information useful for analysis?

Artificial intelligence

Artficial artficial intelligence

  • Amazon Mechanical Turk
  • crowdsourcing marketplace platform
  • fake chess-playing machine (late 18th century)

mTurk HITs (Human Intelligence Tasks)

HITs processing workflow


Using coordinate system of the webcam image:

  • distances in the image represent varying distances in reality
  • we can't integrate other geospatial datasets (streets, POIs) or information from other webcams

Solution is to compute projective transformation by matching 4+ stable features in the webcam image to the same features in the orthophoto.

Georeferencing: example

Caveats: some webcams change orientation, many objects such as benches, traffic marking are unsuitable as GCPs, stable objects such as statues can move too

STC visualization

Space-time density of pedestrians represented as a 3D volume, computed using multivariate Kernel Density Estimation (KDE) with different spatial and temporal bandwidths

Pedestrian density visualization

webcam 9706 (July), Ehingen, Germany

Effects of plaza reconstruction

webcam 3760 in 2012 (Jul - Sep), Victoria Square, Adelaide, Australia

Effects of plaza reconstruction

webcam 3760 in 2014 (Jul - Sep), Victoria Square, Adelaide, Australia

Change in pedestrian density (2014 minus 2012)

Positive values ~ increase in density in 2014
Negative values ~ decrease in density in 2014

High density of pedestrians and vehicles (webcam 5599)

if (P > percentileP99 AND V > percentileV99, V + P)


  • Python libraries
  • Jupyter Notebook for data exploration
  • Georeferencing: scikit-image, GRASS GIS
  • KDE: SciPy, Statsmodels
  • Rendering: GRASS GIS, ParaView, Blender

Visualization: GRASS GIS

Visualization: ParaView

webcam 9706 (July), Ehingen, Germany

Visualization: Blender


Visualization: Blend4Web

Conclusion & Future work

  • new method for harvesting and visualization of spatio-temporal information about active transportation
  • new way for cities to detect and analyze changes in active transportation behavior in an unintrusive way
  • georeferenced data give us the ability to incorporate other geospatial data and methods (e.g., solar radiation modeling)
  • possible thanks to the synergy between crowdsourcing technologies (AMOS, mTurk, open source software)
  • machine learning techniques trained by mTurk data will enable us to analyze much larger data volume in real-time, possibly leading to the discovery of more patterns


Challenges: webcam geometry and view

  • areas hidden behind trees or other objects
  • assumes pedestrians and vehicles on a horizontal plane, otherwise we get large spatial errors

Challenges: mTurk reliability

Traffic lights, statues mistakenly marked as pedestrians, machine learning approaches would avoid this type of error


  • Hipp, J. A., Adlakha, D., Gernes, R., Kargol, A., Pless, R., Drive, O. B., Louis, S. (2013). Do You See What I See: Crowdsource Annotation of Captured Scenes, 24–25.
  • Hipp, J. A., Manteiga, A., Burgess, A., Stylianou, A., Pless, R. (2016). Webcams, Crowdsourcing, and Enhanced Crosswalks: Developing a Novel Method to Analyze Active Transportation. Front. Public Health, 4(97).
  • Jacobs, N., Roman, N., Pless, R. (2007). Consistent temporal variations in many outdoor scenes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.