The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.07 - July (2008 vol.30)
pp: 1308-1312
ABSTRACT
We advocate the use of Markov sequential object processes for tracking a variable number of moving objects through video frames with a view towards depth calculation. A regression model based on a sequential object process quantifies goodness of fit; regularization terms are incorporated to control within and between frame object interactions. We construct a Markov chain Monte Carlo method for finding the optimal tracks and associated depths and illustrate the approach on a synthetic data set as well as a sport sequence.
INDEX TERMS
Vision and Scene Understanding, Motion
CITATION
M.N.M. van Lieshout, "Depth Map Calculation for a Variable Number of Moving Objects using Markov Sequential Object Processes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 7, pp. 1308-1312, July 2008, doi:10.1109/TPAMI.2008.45
REFERENCES
[1] A.J. Baddeley and M.N.M. van Lieshout, “Object Recognition Using Markov Spatial Processes,” Proc. 11th IAPR Int'l Conf. Pattern Recognition, pp. B136-B139, 1992.
[2] A.J. Baddeley and M.N.M. van Lieshout, “Stochastic Geometry Models in High-Level Vision,” Statistics and Images, Vol. 1, vol. 20, K.V. Mardia and G.K. Kanji, eds., Advances in Applied Statistics, a supplement to J. Applied Statistics, pp. 231-256, 1993.
[3] R.L. Eubank, A Kalman Filter Primer. Chapman & Hall/CRC, 2006.
[4] C.J. Geyer and J. Møller, “Simulation Procedures and Likelihood Inference for Spatial Point Processes,” Scandinavian J. Statistics, vol. 21, pp. 359-373, 1994.
[5] I.R. Goodman, R.P.S. Mahler, and H.T. Nguyen, Mathematics of Data Fusion, Vol. 39 of Series B: Mathematical and Statistical Methods, Kluwer, 1997.
[6] N. Gordon, D. Salmond, and A. Smith, “Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation,” IEE Proc., vol. 140, pp. 107-113, 1993.
[7] P.J. Green, “Reversible Jump MCMC Computation and Bayesian Model Determination,” Biometrika, vol. 82, pp. 711-732, 1995.
[8] P.V.C. Hough, “Method and Means for Recognizing Complex Patterns,” US Patent 3069654, 1962.
[9] C. Hue, J.-P. Le Cadre, and P. Pérez, “Sequential Monte Carlo Methods for Multiple Target Tracking and Data Fusion,” IEEE Trans. Signal Processing, vol. 50, pp. 309-325, 2002.
[10] J. Illingworth and J. Kittler, “A Survey of the Hough Transform,” Computer Vision, Graphics, and Image Processing, vol. 44, pp. 87-116, 1988.
[11] R. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” J. Basic Eng., vol. 82, pp. 35-45, 1960.
[12] Z. Khan, T. Balch, and F. Dellaert, “MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, pp. 1805-1819, 2005.
[13] C. Lacoste, X. Descombes, and J. Zerubia, “Point Processes for Unsupervised Line Network Extraction in Remote Sensing,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, pp. 1568-1579, 2005.
[14] M.N.M. van Lieshout, “Markovianity in Space and Time,” Dynamics & Stochastics: Fest-Schrift in honor of M.S. Keane, D. Denteneer, F. den Hollander, and E. Verbitskiy, eds., Lecture Notes—Monograph Series, Inst. for Math. Statistics, vol. 48, pp. 154-168, 2006.
[15] M.N.M. van Lieshout, “Campbell and Moment Measures for Finite Sequential Spatial Processes,” Proc. Prague Stochastics 2006, M. Hušková and M. Janžura, eds., pp. 215-224, 2006.
[16] J. Lund, A. Penttinen, and M. Rudemo, “Bayesian Analysis of Spatial Point Patterns from Noisy Observations,” research report, Dept. of Math. and Physics, The Royal Veterinary and Agricultural Univ., 1999.
[17] K.V. Mardia, W. Qian, D. Shah, and K.M.A. Desouza, “Deformable Template Recognition of Multiple Occluded Objects,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 1035-1042, Sept. 1997.
[18] G. Matheron, Random Sets and Integral Geometry. John Wiley and Sons, 1975.
[19] R. Molina and B.D. Ripley, “Using Spatial Models as Priors in Astronomical Image Analysis,” J. Applied Statistics, vol. 16, pp. 193-206, 1989.
[20] B.D. Ripley and F.P. Kelly, “Markov Point Processes,” J. London Math. Soc., vol. 15, pp. 188-192, 1977.
[21] B.D. Ripley and A.I. Sutherland, “Finding Spiral Structures in Images of Galaxies,” Philosophical Trans. Royal Soc. London, Series A, vol. 332, pp. 477-485, 1990.
[22] C.P. Robert and G. Casella, Monte Carlo Statistical Methods. Springer, 1999.
[23] H. Rue and M.A. Hurn, “Bayesian Object Identification,” Biometrika, vol. 86, pp. 649-660, 1999.
[24] R. Stoica, X. Descombes, and J. Zerubia, “A Gibbs Point Process for Road Extraction in Remotely Sensed Images,” Int'l J. Computer Vision, vol. 57, pp.121-136, 2004.
[25] L.D. Stone, C.A. Barlow, and T.L. Corwin, Bayesian Multiple Target Tracking. Artech House, 1999.
[26] M. Vihola, “Random Sets for Multitarget Tracking and Data Fusion,” licentiate thesis, Tampere Univ. of Tech nology, 2004.
16 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool