A particle filter to track multiple objects

TL;DR: A robust sound source localization and tracking method using an array of eight microphones based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm that allows a mobile robot to interact using more natural means with people in real-life settings.

TL;DR: A new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes is proposed.

TL;DR: MeMOT as mentioned in this paper proposes an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects.

TL;DR: An online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span is proposed, by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects.

TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.

TL;DR: A new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter.

Abstract: Abstract For missing data problems, Tanner and Wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors. Their method of constructing the complete data sets is closely related to the Gibbs sampler. Both required iterations, and, similar to the EM algorithm, convergence can be slow. We introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights. In many applications this new procedure works very well without the need for iterations. Sensitivity analysis, influence analysis, and updating with new data can be performed cheaply. Bayesian prediction and model selection can also be incorporated. Examples taken from a wide range of applications are used for illustration.

TL;DR: In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken and the PMHT algorithm is taken, which is computationally practical because it requires neither enumeration of measurement- to-track assignments nor pruning.