Abstract
We present a model for unconstrained and unobtrusive identification and tracking of people in smart environments and answering queries about their whereabouts. Our model supports biometric recognition based upon multiple modalities such as face, gait, and voice in a uniform manner. The key technical idea underlying our approach is to abstract a smart environment by a state transition system in which each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is inherently probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to integrate different biometric modalities uniformly and also different criteria for state transitions. Fusion of biometric modalities is also supported by our model. We define performance metrics for a smart environment in terms of the concepts of ‘precision’ and ‘recall’. We have developed a prototype implementation of our proposed concepts and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a good basis for developing practical systems.
Similar content being viewed by others
Notes
Face images blurred to preserve anonymity.
References
Aghajan HK, Augusto JC, Wu C, McCullagh PJ, Walkden J (2007) Distributed vision-based accident management for assisted living. In: Okadome T, Yamazaki T, Makhtari M (eds) ICOST ’07, lecture notes in computer science, vol 4541. Springer, Berlin, pp 196–205
Bar-Shalom Y, Li X (1995) Multitarget-multisensor tracking: principles and techniques. Yaakov Bar-Shalom
Bernardin K, Stiefelhagen R, Waibel A (2008) Probabilistic integration of sparse audio-visual cues for identity tracking. In: Proceedings of the 16th ACM international conference on multimedia (MM ’08), ACM, pp 151–158.
Bouchaffra D, Govindaraju V, Srihari S (1999) A methodology for mapping scores to probabilities. IEEE Trans Pattern Anal Mach Intell 21(9):923–927
Bui HH, Venkatesh S, West G (2001) Tracking and surveillance in wide-area spatial environments using the abstract Hidden Markov Model. Int J Pattern Recogn AI 15(1):177–195
Cao H, Govindaraju V (2007) Vector model based indexing and retrieval of handwritten medical forms. In: Proceedings of the international conference on document analysis and recognition (ICDAR ’07), IEEE Computer Society, pp 88–92
Das SK, Roy N, Roy A (2006) Context-aware resource management in multi-inhabitant smart homes: a framework based on Nash H-learning. Pervasive Mob Comput 2(4):372–404
Ekenel HK, Fischer M, Jin Q, Stiefelhagen R (2007) Multi-modal Person identification in a smart environment. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR ’07), IEEE Computer Society, pp 1–8
Fox D, Hightower J, Liao L, Schulz D, Borriello G (2003) Bayesian filtering for location estimation. IEEE Pervasive Comput 2(3):24–33
Hewitt R (2007) Seeing with openCV: implementing eigenface. SERVO Magazine, pp 44–50
Hewitt R, Belongie S (2006) Active learning in face recognition: using tracking to build a face model. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshop (CVPRW ’06), IEEE Computer Society, p 157
Hightower J, Borriello G (2001) Location systems for ubiquitous computing. IEEE Comput 34(8):57–66
Krumm J, Harris S, Meyers B, Brumitt B, Hale M, Shafer S (2000) Multi-camera multi-person tracking for easyliving. In: Proceedings of the third IEEE international workshop on visual surveillance, IEEE Computer Society, p 3
Luque J et al (2007) Audio, video and multimodal person identification in a smart room. In: Stiefelhagen R, Garofolo J (eds) Multimodal technologies for perception of humans. Lecture notes in computer science, vol 4122. Springer, USA, pp 258–269
Manesis T, Avouris N (2005) Survey of position location techniques in mobile systems. In: Proceedings of the 7th international conference on human computer interaction with mobile devices and services (MobileHCI ’05), ACM, pp 291–294
Menon V, Jayaraman B, Govindaraju V (2008) Biometrics driven smart environments: abstract framework and evaluation. In: Proceedings of the 5th international conference on ubiquitous intelligence and computing (UIC ’08), Springer, Berlin, pp 75–89
Menon V, Jayaraman B, Govindaraju V (2008) Integrating recognition and reasoning in smart environments. In: Proceedings of the 4th IET international conference on intelligent environments (IE ’08), pp 1–8
Misra A, Das SK (2005) Location estimation (determination and prediction) techniques in smart environments. In: Smart P (eds) Environments: technology, applications, ambient intelligence. Wiley-Interscience, pp 193–228
OpenCV, http://www.intel.com/technology/computing/opencv/index.htm
Pentland A, Choudhury T (2000) Face recognition for smart environments. IEEE Comput 33(2):50–55
van Rijsbergen CJ (1979) Information retrieval. Butterworths, London
Rumbaugh J, Jacobson I, Booch G (2004) Unified modeling language reference manual, 2nd edn. Addison-Wesley Professional, Reading
Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Pers Commun 8(4):10–17
Schulz D, Fox D, Hightower J (2003) People tracking with anonymous and Id-sensors using Rao-Blackwellised particle filters. In: Proceedings of the 18th international joint conference on artificial intelligence (IJCAI ’03), pp 921–926
Tulyakov S, Wu C, Govindaraju V (2009) On the difference between optimal combination functions for verification and identification systems. Intern J Pattern Recognit Artif Intell
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
Weiser M (1991) The computer for the 21st century. Sci Am 265(3):66–75
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4)
Zhang S, Janakiraman R, Sim T, Kumar S (2005) Continuous verification using multimodal biometrics. In: Zhang D, Jain AK (eds) Advances in biometrics, lecture notes in computer science, vol 3832. Springer, Berlin, pp 562–570
Acknowledgments
This work was done while Vivek Menon was a Visiting Research Scientist at the Center for Unified Biometrics and Sensors (CUBS), University at Buffalo. Thanks to Philip Kilinskas for his help in developing the experimental prototype; Dr. Jason J. Corso for discussions on Markov models; and members of CUBS for their comments and suggestions on an earlier version of this paper [16].
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Menon, V., Jayaraman, B. & Govindaraju, V. Multimodal identification and tracking in smart environments. Pers Ubiquit Comput 14, 685–694 (2010). https://doi.org/10.1007/s00779-010-0288-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-010-0288-6