MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements.

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.

MCMC-based particle filtering for tracking a variable number of interacting targets.

We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.

An outdoor 3-D visual tracking system for the study of spatial navigation and memory in rhesus monkeys.

Previous studies of the navigational abilities of nonhuman primates have largely been limited to what could be described by a human observer with a pen and paper. Consequently, we have developed a system that uses a pair of cameras to automatically obtain the three-dimensional trajectory of rhesus monkeys performing an outdoor spatial navigation and memory task. The system provides trajectories, path length, speed, and other variables that would be impossible for an unaided observer to note. From trajectory data, we computed and validated a path-length measurement. We use this measurement to compare the navigation abilities of several animals. In addition, we provide quantitative data on the accuracy of a method for automatic behavior detection. Currently, the system is being used to examine the sex differences in spatial navigation of rhesus monkeys. We expect that measures derived from the trajectory data will reveal strategies used by animals to solve spatial problems.