Video surveillance using raspberry Pi with particle filtering

  • Dipali Ambadas Bhor University of Pune
  • G U Kharat
Keywords: video survillance, opencv, rasberry pi, camera module, particle filter, object detection

Abstract

—One of the fundamental problem in vision is that of tracking objects through sequences of images. Within this report the design of Particle filter algorithm to track the target and show the resulting improvement in tracking. More specifically, this project described the technique of how to track Moving Object. The proposed architecture is suitable for indoors and outdoors scenes with static background and overcomes the problem of stationary targets fading into the background. Optimal estimation problems for nonlinear non-Gaussian state-space models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer. The real time video surveillance using a system equipped for sensor support data is being developed to provide situational in the target of interest & also provide a safe means of video surveillance in the target place using raspberry pi.

References

[1] Anderson, B. D. O. and Moore, J. B.(1979) Optimal Filtering, Prentice Hall, Englewood Clis, New Jersey. [2] Andrieu, C. and Doucet, A. (2002) Particle filtering for partially observed Gaussian state space models. Journal of the Royal Statistical Society B, 64(4), 827-836. [3] Arulampalam, S., Maskell, S., Gordon, N. and Clapp, T. (2002) A tutorial on particle filters for on-line nonlinear/nonGaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174{188. [4] Bresler, Y. (1986) Two-filter formula for discrete-time non-linear Bayesian smoothing. International Journal of Control,43(2), 629{641. [5] Briers, M., Doucet, A. and Maskell, S. (2008) Smoothing algorithms for state-space models, Annals of the Institute of Statistical Mathematics,to appear. [6] Briers, M., Doucet, A. and Singh, S.S. (2005) Sequential auxiliary particle belief propagation, Proceedings Conference Fusion. [7] Carpenter, J., Cli_ord, P. and Fearnhead, P. (1999) An improved particle filter for non-linear problems.IEE proceedings | Radar, Sonar and Navigation, 146, 2{7. [8] Capp_e, O. , Godsill, S. J. and Moulines, E. (2007) An overview of existing methods and recent advances in sequential Monte Carlo. IEEE Proceedings, 95(5):899{924. [9] Chopin, N. (2004) Central limit theorem for sequential Monte Carlo and its application to Bayesian inference. Ann. Statist., 32, 2385-2411. [10] Crisan, D. and Doucet, A. (2002) A survey of convergence results on particle filtering for practitioners. IEEE Transactions on Signal Processing,50(3), 736{746. [11] Del Moral, P. (2004) Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applica- tions. Series: Probability and Applications, Springer-Verlag, New York. [12] Del Moral, P., Doucet, A. and Jasra, A. (2008) On adaptive resampling procedures for sequential Monte Carlo methods. Technical report INRIA. [13] Douc, R., Capp_e, O. and Moulines, E. (2005) Comparison of resampling schemes for particle filtering. In 4th International Symposium on Image and Signal Processing and Analysis (ISPA). [14] Doucet, A., Godsill, S. J. and Andrieu, C. (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing,10, 197{208. [15] Doucet, A., Briers, M. and Senecal, S. (2006) E_cient block sampling strategies for sequential Monte Carlo. Journal of Computational and Graphical Statistics,15(3), 693{711. [16] Doucet, A., de Freitas, N. and Gordon, N.J. (eds.) (2001) Sequential Monte Carlo Methods in Practice. SpringerVerlag, New York. [17] Doucet, A., de Freitas, N. and Gordon, N.J. (2001) An introduction to sequential Monte Carlo methods. In [16], 3{13. [18] Fearnhead, P. (2008) Computational methods for complex stochastic systems: A review of some alternatives to MCMC. Statistics and Computing,18, 151{171. [19] Fearnhead, P., Wyncoll, D. and Tawn, J. (2008) A sequential smoothing algorithm with linear computational cost. Technical report,Department of Mathematics and Statistics, Lancaster University. [20] Gilks, W.R. and Berzuini, C. (2001). Following a moving target -Monte Carlo inference for dynamic Bayesian models. Journal of the Royal Statistical Society B,63, 127{146. [21] Godsill, S.J. and Clapp, T. (2001) Improvement strategies for Monte Carlo particle filters, in [16], 139-158. [22] Gordon N.J., Salmond D.J. and Smith A.F.M. (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation.IEE-Proceedings-F, 140, 107{113. [23] Johansen, A.M. and Doucet, A. (2008) A note on auxiliary particle filters. Statistics and Probability Letters, 78(12),1498{1504.
Published
2018-03-22
How to Cite
Bhor, D., & Kharat, G. (2018). Video surveillance using raspberry Pi with particle filtering. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 3(3). Retrieved from http://asianssr.org/index.php/ajct/article/view/130
Issue
Vol 3 (2017): Issue I
Section
Computer Science and Engineering

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