Tracking multiple objects with particle filtering

By Carine Hue, Jean-Pierre Le Cadre, Patrick Pérez. 2002

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/46b36bccf0684bcad4277844b4aba3ca784fd713

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

1. B. Vo
2. Fredrik K. Gustafsson
3. Arnaud Doucet
4. Patrick Pérez
5. Simon J. Godsill
6. Wing-Kin Ma
7. Sumeetpal S. Singh
8. Jaco Vermaak

In Computer Networks and Communications

1. B. Vo
2. Arnaud Doucet
3. Jaco Vermaak
4. Wing-Kin Ma
5. Ting Yu
6. Ying Wu
7. Patrick Pérez
8. Simon J. Godsill

In Signal Processing

1. Patrick Pérez
2. Jaco Vermaak
3. Arnaud Doucet
4. B. Vo
5. Simon J. Godsill
6. Ting Yu
7. Ying Wu
8. Wing-Kin Ma

Citations in the corpus

• Sequential Monte Carlo methods for multitarget filtering with random finite sets (2005, methodology): “SMC methods are powerful tools in Bayesian filtering [1], [10], [11], [16] and have been applied to multi-target problems [3], [12], [21], [22], [30].”
• A Boosted Particle Filter: Multitarget Detection and Tracking (2004, background): “Among others, Hue et. al [ 5 ] developed a system for multitarget tracking by expanding the state dimension to include component information, assigned by a Gibbs sampler.”
• Maintaining multimodality through mixture tracking (2003, methodology): “The remainder of this section shows how this non-parametric mixture representation can be updated recursively in the same fashion as the two step approach for standard Bayesian sequential estimation.”
• Decentralized multiple target tracking using netted collaborative autonomous trackers (2005, methodology): “For example, the multiple hypothesis tracker (MHT) [2, 5] and the joint probabilistic data association filter (JPDAF) [13] have to exhaust all possible associations, and the sampling-based methods [ 6 , 7, 11, 16, 21] demand a huge number of particles.”
• The Gaussian Mixture Probability Hypothesis Density Filter (2006, methodology): “The joint probabilistic data association filter (JPDAF) [1], [8], the probabilistic MHT (PMHT) [9], and the multiple-target particle filter [3], [4] use observations weighted by their association probabilities.”
• Particle filters for positioning, navigation, and tracking (2002, background): “To which object should a certain measurement be associated? This is a discrete problem, and attempts to include this in a particle filter framework are described in [2], [7], [18], [20], [27], [32], [38], and [41].”
• Monte Carlo filtering for multi target tracking and data association (2005, methodology): “In [28] a method is described that computes the distribution of the association hypotheses using a Gibbs sampler [29] at each time step.”
• Convolution Particle Filter for Parameter Estimation in General State-Space Models (2009, background): “advanced problems like multiple targets tracking and association, or maneuvering targets, one can consult [13, 15, 2, 19, 3].”
• Sequential Monte Carlo methods for multiple target tracking and data fusion (2002, background): “Further details on the motivations for the different ingredients of the MTPF can be found in [18].”