Rao-Blackwellized particle filter for multiple target tracking
By Simo Särkkä, Aki Vehtari, Jouko Lampinen. Information Fusion, 2007
318 citations recorded by the source corpus.
In this article we propose a new Rao-Blackwellized particle filtering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle filtering, and the efficiency of the Monte Carlo sampling is improved by using Rao-Blackwellization.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/668940248d46526f52c0d129b95f1569488266f7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Anna Petrovskaya
- Sebastian Thrun
- Hao Zhu
- Henry Leung
- Simo Särkkä
- Ka-Veng Yuen
- Lyudmila S. Mihaylova
- Vaishak Belle
In Computer Networks and Communications
- Sebastian Thrun
- Anna Petrovskaya
- Simo Särkkä
- Sebastian A. Scherer
- Daniel Maturana
- Philippos Mordohai
- Xiaoyan Hu
Citations in the corpus
- Bayesian Filtering and Smoothing (2013, methodology): “Multiple target tracking (Bar-Shalom and Li, 1995; Blackman and Popoli, 1999; Stone et al., 1999; Särkkä et al., 2007b) systems are used for remote surveillance in the cases where there are multiple targets moving at the same time in the same geographical area (see Figure 1.”
- Overview of Environment Perception for Intelligent Vehicles (2017, background): “In contrast to explicit data association, implicit data association tracking approaches output a set of object hypotheses in an implicit way, such as particle filter approaches [132], probability hypothesis density (PHD) filtering [133], multi-target multiBernoulli (MeMBer) filtering [134], [135], a”
- Model based vehicle detection and tracking for autonomous urban driving (2009, methodology): “For example Sarkka et al. (2007) uses a Rao-Blackwellized particle filter (RBPF) for multiple target tracking on simulated data.”