September  2011, 6(3): 521-544. doi: 10.3934/nhm.2011.6.521

Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods

1. 

Wearable Computing Laboratory, Gloriastrasse 35, ETH Zurich, CH-8092 Zurich, Switzerland, Switzerland, Switzerland

2. 

CLU E11, Clausiusstrasse 50, ETH Zurich, CH-8092 Zurich, Switzerland

Received  December 2010 Revised  June 2011 Published  August 2011

Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through pattern recognition techniques.
    In this paper we present a methodological framework for the machine recognition of crowd behavior from on-body sensors, such as those in mobile phones. The recognition of crowd behaviors opens the way to the acquisition of large-scale datasets for the analysis and understanding of crowd dynamics. It has also practical safety applications by providing improved crowd situational awareness in cases of emergency.
    The framework comprises: behavioral recognition with the user's mobile device, pairwise analyses of the activity relatedness of two users, and graph clustering in order to uncover globally, which users participate in a given crowd behavior. We illustrate this framework for the identification of groups of persons walking, using empirically collected data.
    We discuss the challenges and research avenues for theoretical and applied mathematics arising from the mobile sensing of crowd behaviors.
Citation: Daniel Roggen, Martin Wirz, Gerhard Tröster, Dirk Helbing. Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods. Networks & Heterogeneous Media, 2011, 6 (3) : 521-544. doi: 10.3934/nhm.2011.6.521
References:
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[2]

K. Bao and S. Intille, "Activity Recognition from User-Annotated Acceleration Data,", Proc 2nd Int Conf Pervasive Computing, (2004), 1. Google Scholar

[3]

N. Bellomo and C. Dogbé, On the modelling crowd dynamics from scaling to hyperbolic macroscopic models,, Mathematical Models and Methods in Applied Sciences, 18 (2008). doi: 10.1142/S0218202508003054. Google Scholar

[4]

M. Benocci, C. Tacconi, E. Farella, L. Benini, L. Chiari and L. Vanzago, Accelerometer-based fall detection using optimized zigbee data streaming,, Microelectronics Journal, 41 (2010), 703. doi: 10.1016/j.mejo.2010.06.014. Google Scholar

[5]

C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas, A. Ranganathan and D. Riboni, A survey of context modelling and reasoning techniques,, Pervasive and Mobile Computing, 6 (2010), 161. doi: 10.1016/j.pmcj.2009.06.002. Google Scholar

[6]

I. Borg and P Groenen, "Modern Multidimensional Scaling: Theory and Applications,", Springer Series in Statistics, (1997). Google Scholar

[7]

M. Buchanan, The science of subtle signals,, Strategy+Business, 48 (2007), 68. Google Scholar

[8]

I. Cohen and M. Goldszmidt, Properties and benefits of calibrated classifiers,, Proc. Knowledge Discovery in Databases, (2004), 125. Google Scholar

[9]

V. Coscia and C. Canavesio, First-order macroscopic modelling of human crowd dynamics,, Math. Mod. Meth. Appl. Sci., 18 (2008), 1217. doi: 10.1142/S0218202508003017. Google Scholar

[10]

N. Davies, D. P. Siewiorek and R. Sukthankar, Special issue: Activity-based computing,, IEEE Pervasive Computing, 7 (2008), 20. doi: 10.1109/MPRV.2008.26. Google Scholar

[11]

R. O. Duda, P. E. Hart and D. G. Stork, "Pattern Classification,", Second edition, (2001). Google Scholar

[12]

P. Eades, A heuristic for graph drawing,, Congressus Numerantium, 42 (1984), 149. Google Scholar

[13]

N. Eagle, A. Pentland and D. Lazer, Inferring friendship network structure by using mobile phone data,, Proc Natl Acad Sci U S A, 106 (2009), 15274. doi: 10.1073/pnas.0900282106. Google Scholar

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D. Figo, P. Diniz, D. Ferreira and J. Cardoso, Preprocessing techniques for context recognition from accelerometer data,, Pervasive and Mobile Computing, 14 (2010), 645. Google Scholar

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T. M. J Fruchterman and E. M. Reingold, Graph drawing by force-directed placement,, Software - Practice and Experience, 21 (1991), 1129. doi: 10.1002/spe.4380211102. Google Scholar

[17]

M. González and A.-L. Barabási, Complex networks: From data to models,, Nature Physics, 3 (2007), 224. doi: 10.1038/nphys581. Google Scholar

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D. Helbing, L. Buzna, A. Johansson and T. Werner, Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions,, Transportation Science, 39 (2005), 1. doi: 10.1287/trsc.1040.0108. Google Scholar

[19]

D. Helbing, I. Farkas and T. Vicsek, Simulating dynamical features of escape panic,, Nature, 407 (2000). doi: 10.1038/35035023. Google Scholar

[20]

D. Helbing and A. Johansson, "Pedestrian, Crowd and Evacuation Dynamics,", Meyers Encyclopedia of Complexity and Systems Science, (2009). Google Scholar

[21]

D. Helbing and P. Molnár, Social force model for pedestrian dynamics,, Physical Review E, 51 (1995), 4282. doi: 10.1103/PhysRevE.51.4282. Google Scholar

[22]

S. Hoogendoorn and P. Bovy, Simulation of pedestrian flows by optimal control and differential games,, Opt. Cont. Appl. Meth., 24 (2003), 153. doi: 10.1002/oca.727. Google Scholar

[23]

A. Johansson, D. Helbing and H. Z. Al-Abideen, From crowd dynamics to crowd safety: A video-based analysis,, Advances in Complex Systems, 11 (2008), 497. doi: 10.1142/S0219525908001854. Google Scholar

[24]

A. Johansson, D. Helbing and P. S. Shukla, Specification of the social force pedestrian model by evolutionary adjustment to video tracking data,, Advances in Complex Systems, 10 (2007), 271. doi: 10.1142/S0219525907001355. Google Scholar

[25]

S. Kallio, J. Kela, P. Korpipää and J. Mäntyjärvi, User independent gesture interaction for small handheld devices,, International Journal of Pattern Recognition and Artificial Intelligence, 20 (2006), 505. Google Scholar

[26]

A. Kesting, M. Treiber and D. Helbing, Connectivity statistics of store-and-forward intervehicle communication,, IEEE Transactions on Intelligent Transportation Systems, 11 (2010), 172. doi: 10.1109/TITS.2009.2037924. Google Scholar

[27]

J. Kleinberg, The convergence of social and technological networks,, Communications of the ACM, 51 (2008), 66. doi: 10.1145/1400214.1400232. Google Scholar

[28]

M. H. Ko, G. West, S. Venkatesh and M. Kumar, "Online Context Recognition in Multisensor Systems Using Dynamic Time Warping,", Proc. Conf. Intelligent Sensors, (2005), 283. Google Scholar

[29]

N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury and A. T. Campbell, A survey of mobile phone sensing,, IEEE Communications Magazine, 48 (2010), 140. Google Scholar

[30]

D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabási, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy and M. Van Alstyne, Computational social science,, Science, 323 (2009), 721. doi: 10.1126/science.1167742. Google Scholar

[31]

S. Mann, Humanistic computing: "Wearcom" as a new framework and application for intelligent signal processing,, Proceedings of the IEEE \textbf{86} (1998), 86 (1998), 2123. doi: 10.1109/5.726784. Google Scholar

[32]

S. McKeever, J. Ye, L. Coyle and S. Dobson, "Using Dempster-Shafer Theory of Evidence for Situation Inference,", Proceedings of the 4th European conference on Smart sensing and context, (2009), 149. Google Scholar

[33]

T. M. Mitchell, Mining our reality,, Science, 326 (2009), 1644. doi: 10.1126/science.1174459. Google Scholar

[34]

M. Moussaïd, N. Perozo, S. Garnier, D. Helbing and G. Theraulaz, The walking behaviour of pedestrian social groups and its impact on crowd dynamics,, PLoS One, 5 (2010). doi: 10.1371/journal.pone.0010047. Google Scholar

[35]

J.-K. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. A. de Menezes, K. Kaski, A.-L. Barabási and J. Kertész, Analysis of a large-scale weighted network of one-to-one human communication,, New Journal of Physics, 9 (2007). Google Scholar

[36]

J. A. Paradiso, J. Gips, M. Laibowitz, S. Sadi, D. Merrill, R. Aylward, P. Maes and A. Pentland, Identifying and facilitating social interaction with a wearable wireless sensor network,, Personal and Ubiquitous Computing, 4 (2010), 137. doi: 10.1007/s00779-009-0239-2. Google Scholar

[37]

A. Pentland, "Honest Signals: How They Shape Our World,", Bradford Books, (2008). Google Scholar

[38]

A. Pentland, T. Choudhury, N. Eagle and P. Singh, Human dynamics: Computation for organizations,, Pattern Recognition Letters, 26 (2005), 503. doi: 10.1016/j.patrec.2004.08.012. Google Scholar

[39]

H. Qian, Y. Mao, W. Xiang and Z. Wang, Recognition of human activities using SVM multi-class classifier,, Pattern Recognition Letters, 31 (2010), 100. doi: 10.1016/j.patrec.2009.09.019. Google Scholar

[40]

C. Randell and H. Muller, "Context Awareness by Analysing Accelerometer Data,", ISWC 2000: Proc. of the 4th Int'l Symposium on Wearable Computers, (2000), 175. Google Scholar

[41]

A. Ranganathan, J. Al-Muhtadi and R. H. Campbell, Reasoning about uncertain contexts in pervasive computing environments,, Pervasive Computing, 3 (2004), 62. doi: 10.1109/MPRV.2004.1316821. Google Scholar

[42]

S. Saxena, F. Brémond, M. Thonnat and R. Ma, "Crowd Behavior Recognition for Video Surveillance,", Advanced Concepts for Intelligent Vision Systems (Berlin), 5259 (2008), 970. Google Scholar

[43]

S. E. Schaeffer, Graph clustering,, Computer Science Review, 1 (2007), 27. Google Scholar

[44]

S. H. Shin, M. S. Lee, C. G. Park and H. S. Hong, "Pedestrian Dead Reckoning System with Phone Location Awareness Algorithm,", Proc. Position Location and Navigation Symposium (PLANS), (2010), 97. Google Scholar

[45]

T. Starner, J. Weaver and A. Pentland, Real-time American sign language recognition using desk and wearable computer based video,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (1998), 1371. Google Scholar

[46]

T. Stiefmeier, D. Roggen, G. Ogris, P. Lukowicz and G. Tröster, Wearable activity tracking in car manufacturing,, IEEE Pervasive Computing, 7 (2008), 42. doi: 10.1109/MPRV.2008.40. Google Scholar

[47]

J. A. Ward, P. Lukowicz, G. Tröster and T. Starner, Activity recognition of assembly tasks using body-worn microphones and accelerometers,, IEEE Trans. Pattern Analysis and Machine Intelligence, 28 (2006), 1553. Google Scholar

[48]

J. A. Ward, P. Lukowicz and H. Gellersen, Performance metrics for activity recognition,, ACM Transactions on Information Systems and Technology, 2 (2011), 1. Google Scholar

[49]

M. Wirz, D. Roggen and G. Tröster, "Decentralized Detection of Group Formations from Wearable Acceleration Sensors,", IEEE Int. Conf. on Computational Science and Engineering, (2009), 952. Google Scholar

[50]

_____, "A Methodology Towards the Detection of Collective Behavior Patterns by Means of Body-Worn Sensors,", UbiLarge workshop at the 8th Int. Conf. on Pervasive Computing, (2010). Google Scholar

[51]

_____, "User Acceptance Study of a Mobile System for Assistance During Emergency Situations at Large-Scale Events,", 3rd International Conference on Human Centric Computing, (2010). Google Scholar

[52]

P. Zappi, C. Lombriser, E. Farella, D. Roggen, L. Benini and G. Tröster, "Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection,", 5th European Conf. on Wireless Sensor Networks (ed. R. Verdone), (2008), 17. Google Scholar

[53]

B. Zhan, D. N. Monekosso, P. Remagnino, S. A. Velastin and L.-Q. Xu, Crowd analysis: A survey,, Machine Vision and Applications, 19 (2008). doi: 10.1007/s00138-008-0132-4. Google Scholar

show all references

References:
[1]

M. Bächlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi and G. Tröster, Wearable assistant for parkinson's disease patients with the freezing of gait symptom,, IEEE Transactions on Information Technology in Biomedicine, 14 (2010), 436. doi: 10.1109/TITB.2009.2036165. Google Scholar

[2]

K. Bao and S. Intille, "Activity Recognition from User-Annotated Acceleration Data,", Proc 2nd Int Conf Pervasive Computing, (2004), 1. Google Scholar

[3]

N. Bellomo and C. Dogbé, On the modelling crowd dynamics from scaling to hyperbolic macroscopic models,, Mathematical Models and Methods in Applied Sciences, 18 (2008). doi: 10.1142/S0218202508003054. Google Scholar

[4]

M. Benocci, C. Tacconi, E. Farella, L. Benini, L. Chiari and L. Vanzago, Accelerometer-based fall detection using optimized zigbee data streaming,, Microelectronics Journal, 41 (2010), 703. doi: 10.1016/j.mejo.2010.06.014. Google Scholar

[5]

C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas, A. Ranganathan and D. Riboni, A survey of context modelling and reasoning techniques,, Pervasive and Mobile Computing, 6 (2010), 161. doi: 10.1016/j.pmcj.2009.06.002. Google Scholar

[6]

I. Borg and P Groenen, "Modern Multidimensional Scaling: Theory and Applications,", Springer Series in Statistics, (1997). Google Scholar

[7]

M. Buchanan, The science of subtle signals,, Strategy+Business, 48 (2007), 68. Google Scholar

[8]

I. Cohen and M. Goldszmidt, Properties and benefits of calibrated classifiers,, Proc. Knowledge Discovery in Databases, (2004), 125. Google Scholar

[9]

V. Coscia and C. Canavesio, First-order macroscopic modelling of human crowd dynamics,, Math. Mod. Meth. Appl. Sci., 18 (2008), 1217. doi: 10.1142/S0218202508003017. Google Scholar

[10]

N. Davies, D. P. Siewiorek and R. Sukthankar, Special issue: Activity-based computing,, IEEE Pervasive Computing, 7 (2008), 20. doi: 10.1109/MPRV.2008.26. Google Scholar

[11]

R. O. Duda, P. E. Hart and D. G. Stork, "Pattern Classification,", Second edition, (2001). Google Scholar

[12]

P. Eades, A heuristic for graph drawing,, Congressus Numerantium, 42 (1984), 149. Google Scholar

[13]

N. Eagle, A. Pentland and D. Lazer, Inferring friendship network structure by using mobile phone data,, Proc Natl Acad Sci U S A, 106 (2009), 15274. doi: 10.1073/pnas.0900282106. Google Scholar

[14]

T. Fawcett, "ROC graphs: Notes and practical considerations for researchers,", Tech. report, (2004). Google Scholar

[15]

D. Figo, P. Diniz, D. Ferreira and J. Cardoso, Preprocessing techniques for context recognition from accelerometer data,, Pervasive and Mobile Computing, 14 (2010), 645. Google Scholar

[16]

T. M. J Fruchterman and E. M. Reingold, Graph drawing by force-directed placement,, Software - Practice and Experience, 21 (1991), 1129. doi: 10.1002/spe.4380211102. Google Scholar

[17]

M. González and A.-L. Barabási, Complex networks: From data to models,, Nature Physics, 3 (2007), 224. doi: 10.1038/nphys581. Google Scholar

[18]

D. Helbing, L. Buzna, A. Johansson and T. Werner, Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions,, Transportation Science, 39 (2005), 1. doi: 10.1287/trsc.1040.0108. Google Scholar

[19]

D. Helbing, I. Farkas and T. Vicsek, Simulating dynamical features of escape panic,, Nature, 407 (2000). doi: 10.1038/35035023. Google Scholar

[20]

D. Helbing and A. Johansson, "Pedestrian, Crowd and Evacuation Dynamics,", Meyers Encyclopedia of Complexity and Systems Science, (2009). Google Scholar

[21]

D. Helbing and P. Molnár, Social force model for pedestrian dynamics,, Physical Review E, 51 (1995), 4282. doi: 10.1103/PhysRevE.51.4282. Google Scholar

[22]

S. Hoogendoorn and P. Bovy, Simulation of pedestrian flows by optimal control and differential games,, Opt. Cont. Appl. Meth., 24 (2003), 153. doi: 10.1002/oca.727. Google Scholar

[23]

A. Johansson, D. Helbing and H. Z. Al-Abideen, From crowd dynamics to crowd safety: A video-based analysis,, Advances in Complex Systems, 11 (2008), 497. doi: 10.1142/S0219525908001854. Google Scholar

[24]

A. Johansson, D. Helbing and P. S. Shukla, Specification of the social force pedestrian model by evolutionary adjustment to video tracking data,, Advances in Complex Systems, 10 (2007), 271. doi: 10.1142/S0219525907001355. Google Scholar

[25]

S. Kallio, J. Kela, P. Korpipää and J. Mäntyjärvi, User independent gesture interaction for small handheld devices,, International Journal of Pattern Recognition and Artificial Intelligence, 20 (2006), 505. Google Scholar

[26]

A. Kesting, M. Treiber and D. Helbing, Connectivity statistics of store-and-forward intervehicle communication,, IEEE Transactions on Intelligent Transportation Systems, 11 (2010), 172. doi: 10.1109/TITS.2009.2037924. Google Scholar

[27]

J. Kleinberg, The convergence of social and technological networks,, Communications of the ACM, 51 (2008), 66. doi: 10.1145/1400214.1400232. Google Scholar

[28]

M. H. Ko, G. West, S. Venkatesh and M. Kumar, "Online Context Recognition in Multisensor Systems Using Dynamic Time Warping,", Proc. Conf. Intelligent Sensors, (2005), 283. Google Scholar

[29]

N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury and A. T. Campbell, A survey of mobile phone sensing,, IEEE Communications Magazine, 48 (2010), 140. Google Scholar

[30]

D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabási, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy and M. Van Alstyne, Computational social science,, Science, 323 (2009), 721. doi: 10.1126/science.1167742. Google Scholar

[31]

S. Mann, Humanistic computing: "Wearcom" as a new framework and application for intelligent signal processing,, Proceedings of the IEEE \textbf{86} (1998), 86 (1998), 2123. doi: 10.1109/5.726784. Google Scholar

[32]

S. McKeever, J. Ye, L. Coyle and S. Dobson, "Using Dempster-Shafer Theory of Evidence for Situation Inference,", Proceedings of the 4th European conference on Smart sensing and context, (2009), 149. Google Scholar

[33]

T. M. Mitchell, Mining our reality,, Science, 326 (2009), 1644. doi: 10.1126/science.1174459. Google Scholar

[34]

M. Moussaïd, N. Perozo, S. Garnier, D. Helbing and G. Theraulaz, The walking behaviour of pedestrian social groups and its impact on crowd dynamics,, PLoS One, 5 (2010). doi: 10.1371/journal.pone.0010047. Google Scholar

[35]

J.-K. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. A. de Menezes, K. Kaski, A.-L. Barabási and J. Kertész, Analysis of a large-scale weighted network of one-to-one human communication,, New Journal of Physics, 9 (2007). Google Scholar

[36]

J. A. Paradiso, J. Gips, M. Laibowitz, S. Sadi, D. Merrill, R. Aylward, P. Maes and A. Pentland, Identifying and facilitating social interaction with a wearable wireless sensor network,, Personal and Ubiquitous Computing, 4 (2010), 137. doi: 10.1007/s00779-009-0239-2. Google Scholar

[37]

A. Pentland, "Honest Signals: How They Shape Our World,", Bradford Books, (2008). Google Scholar

[38]

A. Pentland, T. Choudhury, N. Eagle and P. Singh, Human dynamics: Computation for organizations,, Pattern Recognition Letters, 26 (2005), 503. doi: 10.1016/j.patrec.2004.08.012. Google Scholar

[39]

H. Qian, Y. Mao, W. Xiang and Z. Wang, Recognition of human activities using SVM multi-class classifier,, Pattern Recognition Letters, 31 (2010), 100. doi: 10.1016/j.patrec.2009.09.019. Google Scholar

[40]

C. Randell and H. Muller, "Context Awareness by Analysing Accelerometer Data,", ISWC 2000: Proc. of the 4th Int'l Symposium on Wearable Computers, (2000), 175. Google Scholar

[41]

A. Ranganathan, J. Al-Muhtadi and R. H. Campbell, Reasoning about uncertain contexts in pervasive computing environments,, Pervasive Computing, 3 (2004), 62. doi: 10.1109/MPRV.2004.1316821. Google Scholar

[42]

S. Saxena, F. Brémond, M. Thonnat and R. Ma, "Crowd Behavior Recognition for Video Surveillance,", Advanced Concepts for Intelligent Vision Systems (Berlin), 5259 (2008), 970. Google Scholar

[43]

S. E. Schaeffer, Graph clustering,, Computer Science Review, 1 (2007), 27. Google Scholar

[44]

S. H. Shin, M. S. Lee, C. G. Park and H. S. Hong, "Pedestrian Dead Reckoning System with Phone Location Awareness Algorithm,", Proc. Position Location and Navigation Symposium (PLANS), (2010), 97. Google Scholar

[45]

T. Starner, J. Weaver and A. Pentland, Real-time American sign language recognition using desk and wearable computer based video,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (1998), 1371. Google Scholar

[46]

T. Stiefmeier, D. Roggen, G. Ogris, P. Lukowicz and G. Tröster, Wearable activity tracking in car manufacturing,, IEEE Pervasive Computing, 7 (2008), 42. doi: 10.1109/MPRV.2008.40. Google Scholar

[47]

J. A. Ward, P. Lukowicz, G. Tröster and T. Starner, Activity recognition of assembly tasks using body-worn microphones and accelerometers,, IEEE Trans. Pattern Analysis and Machine Intelligence, 28 (2006), 1553. Google Scholar

[48]

J. A. Ward, P. Lukowicz and H. Gellersen, Performance metrics for activity recognition,, ACM Transactions on Information Systems and Technology, 2 (2011), 1. Google Scholar

[49]

M. Wirz, D. Roggen and G. Tröster, "Decentralized Detection of Group Formations from Wearable Acceleration Sensors,", IEEE Int. Conf. on Computational Science and Engineering, (2009), 952. Google Scholar

[50]

_____, "A Methodology Towards the Detection of Collective Behavior Patterns by Means of Body-Worn Sensors,", UbiLarge workshop at the 8th Int. Conf. on Pervasive Computing, (2010). Google Scholar

[51]

_____, "User Acceptance Study of a Mobile System for Assistance During Emergency Situations at Large-Scale Events,", 3rd International Conference on Human Centric Computing, (2010). Google Scholar

[52]

P. Zappi, C. Lombriser, E. Farella, D. Roggen, L. Benini and G. Tröster, "Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection,", 5th European Conf. on Wireless Sensor Networks (ed. R. Verdone), (2008), 17. Google Scholar

[53]

B. Zhan, D. N. Monekosso, P. Remagnino, S. A. Velastin and L.-Q. Xu, Crowd analysis: A survey,, Machine Vision and Applications, 19 (2008). doi: 10.1007/s00138-008-0132-4. Google Scholar

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