A Review of the Advances in Population Estimation and Human Behaviour Estimation Using Computer Vision and Mobile Phone Data
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Anurag Unnikrishnan, Anshuman Narayan, HS Guruprasad|
|DOI : 10.14445/22312803/IJCTT-V46P108|
Anurag Unnikrishnan, Anshuman Narayan, HS Guruprasad "A Review of the Advances in Population Estimation and Human Behaviour Estimation Using Computer Vision and Mobile Phone Data". International Journal of Computer Trends and Technology (IJCTT) V46(1):37-41, April 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Owing to the population boom in the past decade, the need for efficient and accurate crowd control techniques have risen by the dozen. To prevent violent riots and stampedes, these techniques can provide useful insight into crowd density and location and can help the authorities in channelling their resources efficiently. This paper is a survey of all the various techniques that incorporate computer vision and mobile traffic data analysis. An understanding of these is vital in establishing a formidable crowd control model.
 Davies C.A, Yin J.H and Velastin S.A,” Crowd Monitoring Using Image Processing”, IEEE Electronic and Communications Engineering Journal, Vol 7, No. 1(Feb), pp 37-47.
 ] Liu, X., and K.C. Clarke, 2002. “Estimation of residential population using high resolution satellite imagery,” Proceedings of the 3rd Symposium on Remote Sensing of Urban Areas (D. Maktav, C. Juergens, and F. Sunar-Erbek, editors), 11–13 June, Istanbul, Turkey (Istanbul Technical University), pp. 153–160.
 Yang J, Jin L and He Y,” Crowd Density and Counting Estimation Based on Image Textural Feature”, Journal of Multimedia, Vol 9, No. 10, October 2014.
 Gupta J.K and Gupta S.K,” Design and Analysis of Crowd Sized Estimation Techniques”, International Journal of Computer Applications, Vol.107, No. 19, December 2014.
 Wang B, Bao H, Yang S and Lou H, “Crowd Density Estimation Based on Texture Feature Extraction”, Journal of Multimedia, Vol. 8, No. 4, August 2013.
 Qing Wen, Chengcheng Jia, Yangquan Yu, Gang Chen, Zhezhou Yu and Chunguang Zhou,” People Number Estimation in the Crowded Scenes Using Texture Analysis Based on Gabor Filter”, Journal of Computational Information Systems 7: 11 (2011) 3754-3763.
 Sjarif N.A.A, Shamsuddin S.M, Hashim S.Z,” Detection of Abnormal Behaviours In Crowd Scene: A Review”, International Journal of Advances in Soft Computing and its Applications, January 2012.
 Andrade E, Fisher R, “Simulation of Crowd Problems for Computer Vision, First International Workshop on Crowd Simulation, vol. 3, pp. 71-80 (2005).
 Gupta J and Gupta S.K,” Approach Based Study of Crowd Size Analysis”, International Journal of Research in Advent Technology, Vol 2, No. 3, March 2014 E-ISSN: 2321-9637.
 Caesar J and Musse S,” Crowd Analysis Using Computer Vision Techniques”, IEEE Signal Processing Magazine, Volume 27, Issue 5, Sept 2010.
 Joshi K.Y and Vohra S.A,” Crowd Behaviour Analysis”, International Journal of Science and Research, ISSN: 2319-7064.
 Sirmacek B and Reinartz P,” Automatic Crowd Analysis from Very High Resolution Satellite Images”,5th International Conferences on Recent Advances in Space Technologies,2011, ISBN:978-1-4244-9616-7.
 D. M. Gavrila. The visual analysis of human movement: A survey. CVIU, 73(1):82–98, 1999.
 Zhang Z and Li M, “Crowd density estimation based on statistical analysis of local intra-crowd motions for public area surveillance” Opt. Eng. 51(4), 047204 (May 02, 2012).
 Stauffer, C. and Grimson, W. 2000. Learning patterns of activity using real time tracking. IEEE Trans. Pattern Analysis Machine Intelligence 22, 8, 747–767.
 S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, “Tracking groups of people,” Computer Vision Image Understanding, vol. 80, no. 1, pp. 42–56, 2000.
 Nishi K, Tsubouchi K, Shimosaka M. Hourly pedestrian population trends estimation using location data from smartphones dealing with temporal and spatial sparsity. In: Proc. of the 22nd ACM Int’l Conf. on Advances in Geographic Information Systems. Dallas/Fort Worth: SIGSPATIAL, 2014. 281?290. [doi: 10.1145/2666310.2666391].
 Herrera, J. C., Work, D. Ban, X., Herring, R. Jacobson, Q. and Bayen, A. Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transportation Research C, 18, pp. 568--583, 2010.
 Raslan H and Elragal A,” Inferring Urban Population Distribution from GSM Data: An Experimental Case Study”, The Seventh International Conference on Advanced Geographic Information Systems, Applications, and Services, February 2015.
 Khodabandelou G, Gauthier V, El-Yacoubi A.M and Fiore M,” Population Estimation from Mobile Network Traffic Metadata”,17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2016, DOI:10.1109/WoWMoM.2016.7523554.
 Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D., Tatem, A. J.: Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences 111(45), 15888–15893 (2014).
 Smith-Clarke C, Mashhadi A and Capra L,” Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Pages 511-520.
 Wesolowski A and Eagle N, “Inferring Human Dynamics in Slums Using Mobile Data”, Technical Report, Santa Fe Institute, 2009.
 Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J (2011) Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 8(8): e1001083. doi: 10.1371/journal.pmed.1001083.
 Wilson R, zu Erbach Schoenberg E, Albert M, Power D, Tudge S, Gonzalez M, Guthrie S, Chamberlain H, Brooks C, Hughes C, Pitonakova L, Buckee C, Lu X, Wetter E, Tatem A, Bengtsson L. Rapid and Near Real-time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLOS Currents Disasters. 2016 Feb 24. Edition1.doi:10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c.
 Frias-Martinez E, Williamson G and Frias-Martinez G,” Agent-Based Modelling of Epidemic Spreading using Social Networks and Human Mobility Patterns.” IEEE Third International Conference on Privacy, Security, Risk and Trust 2011, DOI:10.1109/PASSAT/SocialCom.2011.142.
 Douglass, R.W., Meyer, D.A., Ram, M., Rideout D., Song D.,” High resolution population estimates from telecommunications data” EPJ Data Sci. (2015) 4: 4. doi:10.1140/epjds/s13688-015-0040-6.
 Berlingerio M, Calabrese F, Di Lorenzo G, Nar R, Pinelli F and Sbodio L.M,” All Aboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data”, Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III, pp 663-666.
 B. Work, Daniel and M. Bayen Alexander, Impacts of the Mobile Internet on Transportation Cyberphysical Systems: Traffic Monitoring using Smartphones, National Workshop for Research on High-Confidence Transportation Cyber-Physical Systems: Automotive, Aviation and Rail Washington, DC, November 18-20, 2008.
 Furletti B, Gabbrielli M, Giannotti F, Milli L, Nanni M, Pedreschi D,” Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach”, 47th SIS Scientific Meeting of the Italian Statistical Society Cagliari 2014, ISBN: 978-88-8467-874-4.
 de Jonge E, van Pelt M and Roos M,” Time patterns, geospatial clustering and mobility statistics based on mobile phone network data”, Federal Committee on Statistical Methodology Washington D.C, DOI:10.13140/2.1.1536.1920.
 McPherson, Timothy N. and Brown, M. Estimating Daytime and Nighttime Population Distributions in U.S Cities for Emergency Response Activities. Symposium on Planning, Nowcasting and Forecasting in the Urban Zone, January 2004.
computer vision, GLCM, textural classifier, GPS.