Multiple Object Tracking Using Evolutionary MCMC-Based Particle Algorithms

François Septier (1) , Avishy Carmi (1) , Sze Kim Pang (1) , Simon Godsill (1)

1 Signal Processing Laboratory

Algorithms are presented for detection and tracking of multiple clusters of coordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained.

Mots clés: Particle Filtering/Monte Carlo Methods; Bayesian Methods; Filtering and Smoothing

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

François Septier, Avishy Carmi, Sze Kim Pang, Simon Godsill. Multiple Object Tracking Using Evolutionary MCMC-Based Particle Algorithms. 15th IFAC Symposium on System Identification, (SYSID 2009), Jul 2009, Saint-Malo, France. ⟨10.3182/20090706-3-FR-2004.00132⟩. ⟨hal-00566628⟩