Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking

The probability hypothesis density (PHD) filter is well known for addressing the problem of multiple human tracking for a variable number of targets, and the sequential Monte Carlo implementation of the PHD filter, known as the particle PHD filter, can give state estimates with nonlinear and non-Gaussian models. Recently, Mahler et al. have introduced a PHD smoother to gain more accurate estimates for both target states and number. However, as highlighted by Psiaki in the context of a backward-smoothing extended Kalman filter, with a nonlinear state evolution model the approximation error in the backward filtering requires careful consideration. Psiaki suggests that to minimize the aggregated least-squares error over a batch of data. We instead use the term retrodiction PHD filter to describe the backward filtering algorithm in recognition of the approximation error proposed in the original PHD smoother, and we propose an adaptive recursion step to improve the approximation accuracy. This step combines forward and backward processing through the measurement set and thereby mitigates the problems with the original PHD smoother when the target number changes significantly and the targets appear and disappear randomly. Simulation results show the improved performance of the proposed algorithm and its capability in handling a variable number of targets.

[1]  R. Mahler A Theoretical Foundation for the Stein-Winter "Probability Hypothesis Density (PHD)" Multitarget Tracking Approach , 2000 .

[2]  Emilio Maggio,et al.  Efficient Multitarget Visual Tracking Using Random Finite Sets , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Mikhail J. Atallah,et al.  Privacy Enhancing Technologies, 9th International Symposium, PETS 2009, Seattle, WA, USA, August 5-7, 2009. Proceedings , 2009, Privacy Enhancing Technologies.

[4]  Stephen A. Weis Privacy Enhancing Technologies , 2006, IEEE Security & Privacy Magazine.

[5]  Daniel E. Clark,et al.  Convergence results for the particle PHD filter , 2006, IEEE Transactions on Signal Processing.

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

[7]  Luiz F. O. Chamon Combinations of adaptive filters. , 2015 .

[8]  M. Psiaki Backward-Smoothing Extended Kalman Filter , 2005 .

[9]  Y. Bar-Shalom,et al.  Probability hypothesis density filter for multitarget multisensor tracking , 2005, 2005 7th International Conference on Information Fusion.

[10]  Satnam Singh Dlay,et al.  Social force model aided robust particle PHD filter for multiple human tracking , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  A. Doucet,et al.  Sequential Monte Carlo methods for multitarget filtering with random finite sets , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Yonggang Zhang,et al.  Convex Combination of Adaptive Filters for a Variable Tap-Length LMS Algorithm , 2006, IEEE Signal Processing Letters.

[13]  A. Cantoni,et al.  The Cardinalized Probability Hypothesis Density Filter for Linear Gaussian Multi-Target Models , 2006, Annual Conference on Information Sciences and Systems.

[14]  Ali H. Sayed,et al.  Combinations of Adaptive Filters: Performance and convergence properties , 2021, IEEE Signal Processing Magazine.

[15]  Ba-Ngu Vo,et al.  On performance evaluation of multi-object filters , 2008, 2008 11th International Conference on Information Fusion.

[16]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.

[17]  Miao Yu,et al.  A Robust student's-t distribution PHD filter with OCSVM updating for multiple human tracking , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[18]  Thia Kirubarajan,et al.  Multitarget Tracking using Probability Hypothesis Density Smoothing , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Ba-Ngu Vo,et al.  Forward-Backward Probability Hypothesis Density Smoothing , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[20]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[21]  Ba-Ngu Vo,et al.  Visual Tracking in Background Subtracted Image Sequences via Multi-Bernoulli Filtering , 2013, IEEE Transactions on Signal Processing.

[22]  R. Mahler Multitarget Bayes filtering via first-order multitarget moments , 2003 .