Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference u... more 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. iv Stellenbosch University https://scholar.sun.ac.za
Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference u... more 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 allo...
Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference u... more 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. iv Stellenbosch University https://scholar.sun.ac.za
Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference u... more 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 allo...
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Papers by Everhard Louw