# An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark

arXiv: https://arxiv.org/abs/1805.07663 · HTML: https://mlsanity.com/paper/1805.07663/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2018.

## Built on by (most cited first, 16 of 16)

- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [EPIC Fields: Marrying 3D Geometry and Video Understanding](https://mlsanity.com/paper/2306.08731/) (2023)
- [Spectral Temporal Graph Neural Network for Trajectory Prediction](https://mlsanity.com/paper/2106.02930/) (2021)
- [Are socially-aware trajectory prediction models really socially-aware?](https://mlsanity.com/paper/2108.10879/) (2021)
- [It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying](https://mlsanity.com/paper/2209.12890/) (2022)
- [BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2502.14676/) (2025)
- [Under the Hood of Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2203.11878/) (2022)
- [SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2504.15616/) (2025)
- [Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting](https://mlsanity.com/paper/2101.02385/) (2021)
- [Certified Human Trajectory Prediction](https://mlsanity.com/paper/2403.13778/) (2024)
- [Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes](https://mlsanity.com/paper/2101.11472/) (2021)
- [COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos](https://mlsanity.com/paper/2101.08463/) (2021)
- [Toward Smart Doors: A Position Paper](https://mlsanity.com/paper/2209.11770/) (2022)
- [I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras](https://mlsanity.com/paper/2211.09342/) (2022)
- [Predicting Human Trajectories by Learning and Matching Patterns](https://mlsanity.com/paper/2104.10241/) (2021)
- [Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines](https://mlsanity.com/paper/2402.03457/) (2024)

## Abstract (arXiv)

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.