ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 271-277, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/271/2016/
doi:10.5194/isprs-annals-III-3-271-2016
 
03 Jun 2016
A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL
T. Klinger, F. Rottensteiner, and C. Heipke Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hannover, Germany
Abstract. 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.
Conference paper (PDF, 3544 KB)


Citation: Klinger, T., Rottensteiner, F., and Heipke, C.: A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 271-277, doi:10.5194/isprs-annals-III-3-271-2016, 2016.

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