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RED: A simple but effective Baseline Predictor for the TrajNet Benchmark

: Becker, Stefan; Hug, Ronny; Hübner, Wolfgang; Arens, Michael

Postprint urn:nbn:de:0011-n-5310567 (1.2 MByte PDF)
MD5 Fingerprint: 205c9bbb928674c37ee9b1af858cf4bf
The original publication is available at
Erstellt am: 23.01.2020

Leal-Taixé, L.:
Computer Vision - ECCV 2018 Workshops : Munich, Germany, September 8-14, 2018, Proceedings, Part III
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11131)
ISBN: 978-3-030-11014-7 (Print)
ISBN: 978-3-030-11015-4 (Online)
ISBN: 978-3-030-11016-1
European Conference on Computer Vision (ECCV) <15, 2018, Munich>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effectiveness of various deep neural networks for predicting future pedestrian paths are evaluated. The analyzed deep networks solely rely, like in the traditional approaches, on observed tracklets without human-human interaction information. The evaluation is done on the publicly available TrajNet benchmark dataset [39], which builds up a repository of considerable and popular datasets for trajectory prediction. We show how a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve top-rank at the TrajNet 2018 challenge compared to elaborated models. Further, we investigate failure cases and give explanations for observed phenomena, and give some recommendations for overcoming demonstrated shortcomings.