Sequential Monte Carlo Methods for Joint Detection and Tracking of Multiaspect Targets in Infrared Radar Images

We present in this paper a sequential Monte Carlo methodology for joint detection and tracking of a multiaspect target in image sequences. Unlike the traditional contact/association approach found in the literature, the proposed methodology enables integrated, multiframe target detection and tracking incorporating the statistical models for target aspect, target motion, and background clutter. Two implementations of the proposed algorithm are discussed using, respectively, a resample-move (RS) particle filter and an auxiliary particle filter (APF). Our simulation results suggest that the APF configuration outperforms slightly the RS filter in scenarios of stealthy targets.

[1]  Branko Ristic,et al.  A color-based particle filter for joint detection and tracking of multiple objects , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[2]  Marcelo G. S. Bruno.  Bayesian methods for multiaspect target tracking in image sequences , 2004, IEEE Transactions on Signal Processing.

[3]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Neil J. Gordon,et al.  Bayesian State Estimation for Tracking and Guidance Using the Bootstrap Filter , 1993 .

[5]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[6]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[7]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[8]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .

[9]  Christian P. Robert,et al.  Monte Carlo Statistical Methods (Springer Texts in Statistics) , 2005 .

[10]  Rong Chen,et al.  A Theoretical Framework for Sequential Importance Sampling with Resampling , 2001, Sequential Monte Carlo Methods in Practice.

[11]  Rama Chellappa,et al.  A generic approach to simultaneous tracking and verification in video , 2002, IEEE Trans. Image Process..

[12]  J. Bounds.  The infrared airborne radar sensor suite , 1996 .

[13]  J. Geweke,et al.  Bayesian Inference in Econometric Models Using Monte Carlo Integration , 1989 .

[14]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[15]  Simon J. Godsill,et al.  Multitarget tracking using a new soft-gating approach and sequential Monte Carlo methods , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[16]  Nikhil Balram,et al.  Recursive structure of noncausal Gauss-Markov random fields , 1992, IEEE Trans. Inf. Theory.

[17]  Dariu Gavrila,et al.  A Bayesian Framework for Multi-cue 3D Object Tracking , 2004, ECCV.

[18]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[19]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[20]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[21]  Simon J. Godsill,et al.  Tracking variable number of targets using sequential monte carlo methods , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[22]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[23]  José M. F. Moura,et al.  DCT/DST and Gauss-Markov fields: conditions for equivalence , 1998, IEEE Trans. Signal Process..

[24]  N. Balram,et al.  Noncausal Gauss Markov random fields: Parameter structure and estimation , 1993, IEEE Trans. Inf. Theory.

[25]  José M. F. Moura,et al.  Hyperspectral imagery: Clutter adaptation in anomaly detection , 2000, IEEE Trans. Inf. Theory.

[26]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[27]  Jose M. F. Moura,et al.  Multiframe detector/tracker: optimal performance , 2001 .

[28]  José M. F. Moura,et al.  Optimal multiframe detection and tracking in digital image sequences , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[29]  M.G.S. Bruno,et al.  Sequential Monte Carlo filtering for multi-aspect detection/tracking , 2005, 2005 IEEE Aerospace Conference.

[30]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[31]  José M. F. Moura,et al.  Multiframe Bayesian tracking of cluttered targets with random motion , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[32]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[33]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[34]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.