Tracking, Identification and Classification of Random Finite Sets
MetadataShow full item record
This paper considers the problem of joint multiple target tracking, identification, and classification. Standard approaches tend to treat the tasks of data association, estimation, track management and classification as separate problems. This paper outlines how it is possible to formulate a unified a Bayesian recursion for joint tracking, identification and classification. The formulation is based on the theory of random finite sets or finite set statistics, and specifically labeled random finite sets, which results in a propagation of a multi-target posterior which contains not only target information but all available track information. Implementations are briefly discussed. Where appropriate for particular applications this method can be considered Bayes optimal.
Showing items related by title, author, creator and subject.
Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western AustraliaRobinson, Todd Peter (2008)Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect ...
Wundersitz, D.; Josman, C.; Gupta, R.; Netto, Kevin; Gastin, P.; Robertson, S. (2015)Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. ...
Tang, X.; Chen, X.; McDonald, M.; Mahler, Ronald; Tharmarasa, R.; Kirubarajan, T. (2015)© 1991-2012 IEEE. Most conventional target tracking algorithms assume that one target can generate at most one detection per scan. However, in many practical target tracking applications, one target may generate multiple ...