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

Stefan Becker, Ronny Hug, Wolfgang Hubner, Michael Arens; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Abstract


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 toprank 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.

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[bibtex]
@InProceedings{Becker_2018_ECCV_Workshops,
author = {Becker, Stefan and Hug, Ronny and Hubner, Wolfgang and Arens, Michael},
title = {RED: A simple but effective Baseline Predictor for the TrajNet Benchmark},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}