Multiple object tracking using a transform space

Journal Article (2022)
Author(s)

M. Li (Nanjing University of Aeronautics and Astronautics)

J. Li (Nanjing University of Aeronautics and Astronautics)

A. Tamayo (Nanjing University of Aeronautics and Astronautics)

L. Nan (TU Delft - Urban Data Science)

Research Group
Urban Data Science
Copyright
© 2022 M. Li, J. Li, A. Tamayo, L. Nan
DOI related publication
https://doi.org/10.5194/isprs-Annals-V-4-2022-137-2022
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 M. Li, J. Li, A. Tamayo, L. Nan
Research Group
Urban Data Science
Issue number
4
Volume number
5
Pages (from-to)
137-143
Reuse Rights

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Abstract

This paper presents a method for multiple object tracking (MOT) in video streams. The method incorporates the prediction of physical locations of people into a tracking-by-detection paradigm. We predict the trajectories of people on an estimated ground plane and apply a learning-based network to extract the appearance features across frames. The method transforms the detected object locations from image space to an estimated ground space to refine the tracking trajectories. This transform space allows the objects detected from multi-view images to be associated under one coordinate system. Besides, the occluded pedestrians in image space can be well separated in a rectified ground plane where the motion models of the pedestrians are estimated. The effectiveness of this method is evaluated on different datasets by extensive comparisons with state-of-The-Art techniques. Experimental results show that the proposed method improves MOT tasks in terms of the number of identity switches (IDSW) and the fragmentations (Frag).