Visual Detection of Events of Interest from Urban Activity
Learning patterns of human-related activities in outdoor urban spaces, and utilising them to detect activity outliers that represent events of interest, can have important applications in automatic news generation and security. This paper addresses the problem of detecting both expected and unexpected activities in the visual domain. We use a foreground extraction method to mark people and vehicles in videos from city surveillance cameras as foreground blobs. The extracted foreground blobs are then converted to an activity measure to indicate how crowded the scene is at any given video frame. The activity measure, collected over the period of a day, is used to build an activity feature vector describing that day. Day activity vectors are then clustered into different patterns of activities. Common patterns in the data are not considered important as they represent the everyday norm of urban life in that location. Outliers, on the other hand, are detected and reported as events of interest.
KeywordsPatterns of activity Clustering Foreground segmentation
- 3.Bloisi, D., & Iocchi, L. (2012). Independent multimodal background subtraction. In Computational Modelling of Objects Represented in Images-Fundamentals, Methods and Applications III, Third International Symposium, CompIMAGE 2012, Rome, Italy, September 5–7, 2012 (pp. 39–44). doi: 10.1201/b12753-8.
- 5.Godbehere, A. B., Matsukawa, A., & Goldberg, K. Y. (2012). Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In American Control Conference, ACC 2012, Montreal, QC, Canada, June 27–29, 2012 (pp. 4305–4312).Google Scholar
- 6.Goya, Y., Chateau, T., Malaterre, L., & Trassoudaine, L. (2006). Vehicle trajectories evaluation by static video sensors. In 2006 IEEE Intelligent Transportation Systems Conference (pp. 864–869).Google Scholar
- 9.KaewTraKulPong, P., & Bowden, R. (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. In Video-Based Surveillance Systems, chapter 11 (pp. 135–144). US: Springer.Google Scholar
- 13.Noh, S., & Jeon, M. (2013). Computer Vision—ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5–9, 2012, Revised Selected Papers, Part III, chap. A New Framework for Background Subtraction Using Multiple Cues (pp. 493–506). Berlin: Springer.Google Scholar
- 15.Sobral, A. (2013). BGSLibrary: An OpenCV C++ Background Subtraction Library. In IX Workshop de Viso Computacional (WVC’2013). Rio de Janeiro, Brazil.Google Scholar
- 16.Stauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. In 1999 Conference on Computer Vision and Pattern Recognition (CVPR ’99), 23–25 June 1999, Ft. Collins, CO, USA(pp. 2246–2252). doi: 10.1109/CVPR.1999.784637.
- 17.St-Charles, P., & Bilodeau, G. (2014). Improving background subtraction using local binary similarity patterns. In IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA, March 24–26, 2014 (pp. 509–515).Google Scholar
- 18.St-Charles, P., Bilodeau, G., & Bergevin, R. (2014). Flexible background subtraction with self-balanced local sensitivity. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR Workshops 2014, Columbus, OH, USA, June 23–28, 2014 (pp. 414–419).Google Scholar
- 19.Zivkovic, Z. (2004). Improved adaptive Gaussian mixture model for background subtraction. In 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, August 23–26, 2004 (pp. 28–31). doi: 10.1109/ICPR.2004.1333992.