An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
This work provides an incremental analysis for researchers in trajectory prediction, focusing on models without human-human interaction data.
The paper evaluated deep neural networks for pedestrian trajectory prediction using the TrajNet benchmark, showing that a simple RED-predictor model achieved competitive results compared to more complex models.
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, which builds up a repository of considerable and popular datasets for trajectory-based activity forecasting. We show that a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve sophisticated results compared to elaborated models in such scenarios. Further, we investigate failure cases and give explanations for observed phenomena and give some recommendations for overcoming demonstrated shortcomings.