The multi-object Bayes (MOB) filter uses random finite sets (RFSs) to represent a scene. A drawback of this filter is the computational complexity of the multi-object likelihood function. In this contribution, an approximation of the multi-object likelihood function is presented allowing for real-time implementation on a graphics processing unit using sequential Monte Carlo (SMC) methods. Additionally, a track extraction algorithm using clustering as well as an approach to determine the existence probability of each single object are proposed.
Real-Time Multi-Object Tracking using Random Finite Sets
IEEE Transactions on Aerospace and Electronic Systems ; 49 , 4 ; 2666-2678
2013-10-01
2376463 byte
Article (Journal)
Electronic Resource
English
Multi-object tracking using random finite sets
TIBKAT | 2014
|Kinematic sets for real-time robust articulated object tracking
British Library Online Contents | 2007
|Real-time tracking using level sets
IEEE | 2005
|