A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction
- URL: http://arxiv.org/abs/2501.13973v1
- Date: Wed, 22 Jan 2025 19:32:07 GMT
- Title: A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction
- Authors: Juncen Long, Gianluca Bardaro, Simone Mentasti, Matteo Matteucci,
- Abstract summary: The STGN-IT network can predict the future trajectories of pedestrians with incomplete historical trajectories.
A clustering algorithm is also applied in the construction of the network.
Experiments on public datasets show that STGN-IT outperforms state the art algorithms on these metrics.
- Score: 4.267252690168903
- License:
- Abstract: Pedestrian trajectory prediction is important in the research of mobile robot navigation in environments with pedestrians. Most pedestrian trajectory prediction algorithms require the input historical trajectories to be complete. If a pedestrian is unobservable in any frame in the past, then its historical trajectory become incomplete, the algorithm will not predict its future trajectory. To address this limitation, we propose the STGN-IT, a spatio-temporal graph network allowing incomplete trajectory input, which can predict the future trajectories of pedestrians with incomplete historical trajectories. STGN-IT uses the spatio-temporal graph with an additional encoding method to represent the historical trajectories and observation states of pedestrians. Moreover, STGN-IT introduces static obstacles in the environment that may affect the future trajectories as nodes to further improve the prediction accuracy. A clustering algorithm is also applied in the construction of spatio-temporal graphs. Experiments on public datasets show that STGN-IT outperforms state of the art algorithms on these metrics.
Related papers
- Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking [7.9449756510822915]
TrajImpute is a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory.
In this work, we examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories.
Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction.
arXiv Detail & Related papers (2024-10-31T19:42:42Z) - HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention [76.37139809114274]
HPNet is a novel dynamic trajectory forecasting method.
We propose a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions.
Our code is available at https://github.com/XiaolongTang23/HPNet.
arXiv Detail & Related papers (2024-04-09T14:42:31Z) - STF: Spatial Temporal Fusion for Trajectory Prediction [18.359362362173098]
Trajectory prediction is a challenging task that aims to predict the future trajectory of vehicles or pedestrians over a short time horizon.
The more information the model can capture, the more precise the future trajectory can be predicted.
In this study, we introduce an integrated 3D graph that incorporates both spatial and temporal edges.
arXiv Detail & Related papers (2023-11-29T23:31:40Z) - Uncovering the Missing Pattern: Unified Framework Towards Trajectory
Imputation and Prediction [60.60223171143206]
Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences.
Current methods often assume that the observed sequences are complete while ignoring the potential for missing values.
This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
arXiv Detail & Related papers (2023-03-28T14:27:27Z) - Graph-based Spatial Transformer with Memory Replay for Multi-future
Pedestrian Trajectory Prediction [13.466380808630188]
We propose a model to forecast multiple paths based on a historical trajectory.
Our method can exploit the spatial information as well as correct the temporally inconsistent trajectories.
Our experiments show that the proposed model achieves state-of-the-art performance on multi-future prediction and competitive results for single-future prediction.
arXiv Detail & Related papers (2022-06-12T10:25:12Z) - SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory
Prediction [64.16212996247943]
We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction.
Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians.
visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
arXiv Detail & Related papers (2021-04-04T03:17:42Z) - Spatio-Temporal Graph Scattering Transform [54.52797775999124]
Graph neural networks may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.
We put forth a novel mathematically designed framework to analyze-temporal data.
arXiv Detail & Related papers (2020-12-06T19:49:55Z) - Spatial-Temporal Block and LSTM Network for Pedestrian Trajectories
Prediction [0.0]
In this paper, we propose a novel LSTM-based algorithm for trajectory prediction.
We tackle the problem by considering the static scene and pedestrian.
It is LSTM that encodes the relationship so that our model predicts nodes trajectories in crowd scenarios simultaneously.
arXiv Detail & Related papers (2020-09-22T11:43:40Z) - TNT: Target-driveN Trajectory Prediction [76.21200047185494]
We develop a target-driven trajectory prediction framework for moving agents.
We benchmark it on trajectory prediction of vehicles and pedestrians.
We outperform state-of-the-art on Argoverse Forecasting, INTERACTION, Stanford Drone and an in-house Pedestrian-at-Intersection dataset.
arXiv Detail & Related papers (2020-08-19T06:52:46Z) - Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
Reasoning over visual data is a desirable capability for robotics and vision-based applications.
In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent.
Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
arXiv Detail & Related papers (2020-02-20T18:50:44Z) - A Novel Graph based Trajectory Predictor with Pseudo Oracle [15.108410951760131]
GTPPO is an encoder-decoder-based method conditioned on pedestrians' future behaviors.
It is evaluated on ETH, UCY and Stanford Drone datasets.
arXiv Detail & Related papers (2020-02-02T13:40:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.