Title :
MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
Author :
Khan, Zia ; Balch, Tucker ; Dellaert, Frank
Author_Institution :
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA
Abstract :
In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC
Keywords :
Markov processes; Monte Carlo methods; least squares approximations; particle filtering (numerical methods); sampling methods; sensor fusion; state-space methods; target tracking; MCMC data association; Markov chain Monte Carlo; QR factorization; approximate inference; auxiliary variable particle filter; continuous state space; laser range scanner; linear least squares; multiple merged measurements; probabilistic model; real time multitarget tracking; sparse factorization updating; sparse least squares; Computational efficiency; Inference algorithms; Least squares approximation; Least squares methods; Monte Carlo methods; Particle filters; Radar tracking; Sampling methods; State-space methods; Target tracking; Markov chain Monte Carlo; QR factorization; Rao-Blackwellized; downdating; laser range scanner.; linear least squares; merged measurements; multitarget tracking; particle filter; updating; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Movement; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2006.247