Abstract
Multi-agent Based Simulation (MABS) is concerned with the utilisation of agent based technology for the purpose of running simulations of real world scenarios. The challenge is in encoding the agents so that they operate as realistically as possible. The work described in this paper is directed at the mining of movement information from video data which can then be used to encode the operation of agents operating within a MABS framework. More specifically mechanisms are described to firstly mine “movement patterns” from videos of rats contained within in closed environment and secondly to utilise this information in the context of a simple MABS to support the study of animal behaviour.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Opencv-2.3 blob-tracking module. http://www.enl.usc.edu/enl/trunk/aqua/OpenCV-2.3.../Blob_Tracking_Modules.doc
Agiriga, E., Coenen, F., Hurst, J., Beynon, R.J., Kowalski, D.: Towards large-scale multi-agent based rodent simulation: the “mice in a box” scenario. In: Bramer, M., Petridis, M., Nolle, L. (eds.) Research and Development in Intelligent Systems XXVIII, pp. 369–382. Springer, London (2011)
Agiriga, E., Coenen, F., Hurst, J., Kowalski, D.: A multiagent based framework for the simulation of mammalian behaviour. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXX, pp. 435–441. Springer, London (2013)
Banks, J., et al.: Handbook of Simulation. Wiley, New York (1998)
Coenen, F.: Tesseral spatio-temporal reasoning for multi-dimensional data. Pattern Recog. 33(1), 9–23 (2000)
Giancardo, L., Sona, D., Huang, H., Sannino, S., Manag, F., Scheggia, D., Papaleo, F., Murino, V.: Automatic visual tracking and social behaviour analysis with multiple mice. PLoS ONE 8(9), e74557 (2013)
Jhuang, H., Garrote, E., Yu, X., Khilnani, V., Poggio, T., Steele, A.D., Serre, T.: Automated home-cage behavioural phenotyping of mice. Nat. Commun. 1(5), 1–9 (2010)
Leon, G.M., Moreno-Baez, A., Sifuentes-Gallardo, C., Garcia-Dominguez, E., Valencia, M.G.: Analysis of avi files for mice behavior experiments in the morris water maze, pp. 131–136 (2011)
Ohayon, S., Avni, O., Taylor, A.L., Perona, P., Egnor, S.E.R.: Automated multi-day tracking of marked mice for the analysis of social behaviour. J. Neurosci. Methods 219(1), 10–19 (2013)
Vijayakumar, V., Nedunchezhian, R.: A study on video data mining. Int. J. Multimedia Inf. Retrieval 1(3), 153–172 (2012)
Weissbrod, A., Shapiro, A., Vasserman, G., Edry, L., Dayan, M., Yitzhaky, A., Hertzberg, L., Feinerman, O., Kimchi, T.: Automated long-term tracking and social behavioural phenotyping of animal colonies within a semi-natural environment. Nat. Commun. 4 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tufail, M., Coenen, F., Mu, T., Rind, S.J. (2015). Mining Movement Patterns from Video Data to Inform Multi-agent Based Simulation. In: Cao, L., et al. Agents and Data Mining Interaction. ADMI 2014. Lecture Notes in Computer Science(), vol 9145. Springer, Cham. https://doi.org/10.1007/978-3-319-20230-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-20230-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20229-7
Online ISBN: 978-3-319-20230-3
eBook Packages: Computer ScienceComputer Science (R0)