Probability hypothesis density filter using determinantal point processes for multi object tracking

dc.creatorJorquera, Felipe
dc.creatorHernández, Sergio
dc.creatorVergara, Diego
dc.date2023-01-17T13:29:53Z
dc.date2023-01-17T13:29:53Z
dc.date2019
dc.date.accessioned2024-05-02T20:30:24Z
dc.date.available2024-05-02T20:30:24Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/4387
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9274632
dc.descriptionMulti Object Tracking (MOT) has many applications such as video surveillance and event recognition among others. In this paper, we present a novel multi object tracking method using the Probability Density Hypothesis (PHD) filter and Determinantal Point Processes (DPP). The PHD filter is an algorithm for jointly estimating an unknown number of targets and their states from a sequence of observations in the presence of data association uncertainty, noise and false alarms. A tractable implementation of the PHD filter is based on a Gaussian Mixture approximation. However, the Gaussian Mixture PHD suffers from computational problems due to an increasing number of Gaussian components as time progresses. In this paper, we propose a novel pruning method based on Determinantal Point Process which handles the overestimation problem on the number of tracks. The DPP-PHD filter promotes diversity in the resulting Gaussian components and leads to improved tracking results.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceComputer Vision and Image Understanding, 183, 33-41
dc.subjectMulti object tracking
dc.subjectDeterminantal point processes
dc.subjectGaussian mixture
dc.subjectProbability hypothesis density filter
dc.titleProbability hypothesis density filter using determinantal point processes for multi object tracking
dc.typeArticle


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