Abstract
Automatic analysis of how people move about in a particular environment has a number of potential applications. However, no system has so far been able to do detection and tracking robustly. Instead, trajectories are often broken into tracklets. The key idea behind this paper is based around the notion that people need not be detected and tracked perfectly in order to derive useful movement statistics for a particular scene. Tracklets will suffice. To this end we build a tracking framework based on a HoG detector and an appearance-based particle filter. The detector is optimized by learning a scene model allowing for a speedup of the process together with a significantly reduced false positive rate. The developed system is applied in two different scenarios where it is shown that useful statistics can indeed be extracted.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Poulsen, E., Andersen, H., Gade, R., Jensen, O., Moeslund, T.: Using Human Motion Intensity as Input for Urban Design. In: Workshop on Interactive Human Behavior Analysis in Open or Public Spaces, Amsterdam, Holland (2011)
Christensen, P., Mikkelsen, M.R., Alexander, T., Nielsen, S., Harder, H.: Children, Mobility and Space: Using GPS and Mobile Phone Technologies in Ethnographic Research. Journal of Mixed Methods Research 5, 227–246 (2011)
Moeslund, T., Hilton, A., Kruger, V., Sigal, L. (eds.): Visual Analysis of Humans Looking at people. Springer (2011)
Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by Online Learned Discriminative Appearance Models. In: Computer Vision and Pattern Recognition, San Francisco, CA, USA (2010)
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online Multiple Instance Learning. In: Computer Vision and Pattern Recognition (2009)
Moore, B., Ali, S., Mehran, R., Shah, M.: Visual Crowd Surveillance through a Hydrodynamics Lens. Communications of the ACM 54, 64–73 (2011)
Pellegrini, S., Ess, A., Van Gool, L.: Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 452–465. Springer, Heidelberg (2010)
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Online Multi-Person Tracking-by-Detection from a Single, Uncalibrated Camera. IEEE Transactions on Pattern Analysis and Machine Intelligence (in press)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Computer Vision and Pattern Recognition (2005)
Grabner, H., Bischof, H.: Online Boosting and Vision. In: Computer Vision and Pattern Recognition, New York, NY, USA (2006)
Jensen, T., Rasmussen, H.: Automatic Estimation of Statistics on the Movement of People in Complex Indoor Scenes. Master’s thesis. Aalborg University (2011)
Flores, B.E.: A Pragmatic View of Accuracy Measurement in Forecasting. Omega 14, 93–98 (1986)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: The CLEAR MOT metrics. Journal on Image and Video Processing, 1–10 (February 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jensen, T., Rasmussen, H., Moeslund, T.B. (2012). Automatic Estimation of Movement Statistics of People. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds) Articulated Motion and Deformable Objects. AMDO 2012. Lecture Notes in Computer Science, vol 7378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31567-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-31567-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31566-4
Online ISBN: 978-3-642-31567-1
eBook Packages: Computer ScienceComputer Science (R0)