Open AccessA GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL
Author(s) -
T. Klinger,
F. Rottensteiner,
C. Heipke
Publication year - 2016
Publication title -
isprs annals of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 38
eISSN - 2194-9042
pISSN - 2196-6346
DOI - 10.5194/isprsannals-iii-3-271-2016
Subject(s) - benchmark (surveying) , computer science , bittorrent tracker , artificial intelligence , gaussian process , process (computing) , basis (linear algebra) , pedestrian , gaussian , motion (physics) , computer vision , tracking (education) , machine learning , mathematics , eye tracking , geography , psychology , pedagogy , physics , geometry , geodesy , quantum mechanics , operating system , archaeology
Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers