Browsing by Author "Louw, Everhard Johann"

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    A probabilistic graphical model approach to multiple object tracking
    (Stellenbosch : Stellenbosch University, 2018-03) Louw, Everhard Johann; Du Preez, J. A.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference using graphs that correspond to factorised representations of high-dimensional probability distributions. The problem of tracking objects from noisy measurements is inherently a probabilistic one and the use of PGMs to solve this problem is therefore appropriate. In this work, we investigate how PGMs can be used for tracking an unknown and varying number of targets in challenging scenarios. While many existing algorithms provide solutions to the multiple object tracking (MOT) problem, none of the established algorithms are framed as PGMs. In order to develop a graphical model for multiple object tracking, the connections between PGM theory and the Kalman filter algorithm, which is commonly used for single object tracking, are investigated. The PGM equivalent of the Kalman filter is used as a starting point for the development of the MOT PGM. The Kalman filter PGM is first expanded to allow a known and constant number of targets to be tracked, and Bayesian model selection is then used to allow the number of targets to be inferred automatically. In order to allow the model to track targets in the presence of false detections, a clutter classification model is developed and incorporated into the developed PGM. The efficiency of the model is improved through the use of an alternative model selection method. It is also shown that the tracking accuracy can be improved through more accurate Gaussian mixture approximations of the target state distributions. The developed model is compared to a state-of-the-art method and is tested by way of a large number of simulations. We conclude that the model is capable of consistently and accurately tracking targets and that it offers advantages over some existing methods. Finally, the model output is compared to an industrial application with real radar data as input. The outputs of the two models are largely similar and the test results therefore indicate that the developed model can be used for real-world applications. In order to create a general software resource for implementing the type of PGMs designed in this work, the University of Stellenbosch PGM library, EMDW was expanded. A large portion of the software created as part of this work is therefore not limited to the multiple object tracking problem, but useful for PGM inference in general.