Predicting City Safety Perception Based on Visual Image Content

  • Sergio F. Acosta
  • Jorge E. CamargoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Safety perception measurement has been a subject of interest in many cities of the world. This is due to its social relevance, and to its effect on some local economic activities. Even though people safety perception is a subjective topic, sometimes it is possible to find out common patterns given a restricted geographical and sociocultural context. This paper presents an approach that makes use of image processing and machine learning techniques to detect with high accuracy urban environment patterns that could affect citizen’s safety perception.


  1. 1.
    Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition (2013)Google Scholar
  2. 2.
    Dubey, A., Naik, N., Parikh, D., Raskar, R., Hidalgo, C.A.: Deep learning the city: quantifying urban perception at a global scale. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 196–212. Springer, Cham (2016). Scholar
  3. 3.
    Gómez, F., Torres, A., Galvis, J., Camargo, J., Martínez, O.: Hotspot mapping for perception of security. In: IEEE 2nd International Smart Cities Conference: Improving the Citizens Quality of Life, ISC2 2016 - Proceedings, pp. 0–5 (2016)Google Scholar
  4. 4.
    Herbrich, R., Minka, T., Graepel, T.: TrueSkill: a Bayesian skill rating system. In: Advances in Neural Information Processing Systems, vol. 20, pp. 569–576 (2006)Google Scholar
  5. 5.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)Google Scholar
  6. 6.
    Kominers, S.D., et al.: Do People Shape Cities, or Do Cities Shape People? The Co-evolution of Physical, Social, and Economic Change in Five Major U.S. Cities (2015)Google Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1–9 (2012)Google Scholar
  8. 8.
    Naik, N., Philipoom, J., Raskar, R.: Streetscore - Predicting the Perceived Safety of One Million Streetscapes (2014)Google Scholar
  9. 9.
    Ordonez, V., Berg, T.L.: Learning high-level judgments of urban perception. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 494–510. Springer, Cham (2014). Scholar
  10. 10.
    Porzi, L., Buló, S.R., Lepri, B., Ricci, E.: Predicting and Understanding Urban Perception with Convolutional Neural Networks, pp. 139–148 (2015)Google Scholar
  11. 11.
    Salesses, P., Schechtner, K., Hidalgo, C.A.: The collaborative image of the city: mapping the inequality of urban perception. PLoS ONE 8, e68400 (2013)CrossRefGoogle Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.UnSecureLab Research GroupUniversidad Nacional de ColombiaBogotáColombia

Personalised recommendations