# Human-Path-Prediction **Repository Path**: bf19983205/Human-Path-Prediction ## Basic Information - **Project Name**: Human-Path-Prediction - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-04-30 - **Last Updated**: 2025-04-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Human Path Prediction This repository contains the code for the papers: **It is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction**
Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, Adrien Gaidon
Accepted at [ECCV 2020](https://eccv2020.eu/) (Oral) **From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting**
Karttikeya Mangalam*, Yang An*, Harshayu Girase, Jitendra Malik
Accepted to [ICCV 2021](https://iccv2021.thecvf.com/) This repository supports several state of the art pedestrian trajectory forecasting models on both short term (3.2 seconds input, 4.8 seconds ouput) and long term (upto a minute in future) prediction horizons. To train/test models, please visit the PECNet and Ynet folders for model-specific code. Keywords: human path prediction, human trajectory prediction, human path forecasting, pedestrian location forecasting, location prediction, position forecasting, future path forecasting, long term prediction, instantaneous prediction, next second location, multi-agent forecasting, behavior prediction ## Datasets **Stanford Drone Dataset**: - Both Short and Long Term prediction horizon dataloaders - Hand Annotated Segmentation Maps - State of the art prediction trained prediction models as well as training/evaluation code **ETH/UCY Dataset**: - Short term prediction dataloaders - SOTA trajectory prediction trained prediction models for all five scenes - Training/evaluation code for SOTA model and the baselines **InD Dataset**: - Long term prediction dataloaders - Hand Annotated Segmentation Maps - SOTA trajectory prediction trained prediction models as well as training/evaluation code We hope this allows easy benchmarking of several baselines as well as state-of-the-art path prediction models across several datasets and settings. If you find this repository or any code thereof useful in your work, kindly cite: ``` @inproceedings{mangalam2020pecnet, title={It is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction}, author={Mangalam, Karttikeya and Girase, Harshayu and Agarwal, Shreyas and Lee, Kuan-Hui and Adeli, Ehsan and Malik, Jitendra and Gaidon, Adrien}, booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)}, month = {August}, year={2020} } ``` ``` @inproceedings{mangalam2021goals, author = {Mangalam, Karttikeya and An, Yang and Girase, Harshayu and Malik, Jitendra}, title = {From Goals, Waypoints \& Paths To Long Term Human Trajectory Forecasting}, booktitle = {Proc. International Conference on Computer Vision (ICCV)}, year = {2021}, month = oct, month_numeric = {10} } ``` ## Paper Summaries **It is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction**
Published at [ECCV 2020](https://eccv2020.eu/) (Oral) **Abstract**: Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots. In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant trajectory endpoints to assist in long-range multi-modal trajectory prediction. A novel nonlocal social pooling layer enables PECNet to infer diverse yet socially compliant trajectories. Additionally, we present a simple “truncation trick” for improving diversity and multi-modal trajectory prediction performance. Below is an example of pedestrian trajectories predicted by our model and the corresponding ground truth. Left pane shows future trajectories for 9.6 seconds predicted in a recurrent input fashion. Right pane shows the predicted trajectories for future 4.8 seconds at an intersection. Solid circles represent the past input & stars represent the future ground truth. Predicted multi-modal trajectories are shown as translucent circles jointly for all present pedestrians. Animation is best viewed in Adobe Acrobat Reader. More video visualizations available at project homepage: https://karttikeya.github.io/publication/htf/
**From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting**
Karttikeya Mangalam*, Yang An*, Harshayu Girase, Jitendra Malik
Accepted to [ICCV 2021](https://iccv2021.thecvf.com/) **Abstract**: Human trajectory forecasting is an inherently multimodal problem. Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b) sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness in decisions. We propose to factorize this uncertainty into its epistemic & aleatoric sources. We model the epistemic uncertainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in waypoints & paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons upto a minute, upto an order of magnitude longer than prior works. Finally, we present Y-net, a scene compliant trajectory forecasting network that exploits the proposed epistemic & aleatoric structure for diverse trajectory predictions across long prediction horizons. Y-net significantly improves previous state-of-the-art performance on both (a) The short prediction horizon setting on the Stanford Drone (31.7% in FDE) & ETH/UCY datasets (7.4% in FDE) and (b) The proposed long horizon setting on the re-purposed Stanford Drone & Intersection Drone datasets. Below is a GIF visualization demonstrating the goal, waypoint and path multimodality for long term human trajectory prediction (30 seconds horizon). Given the past 5 seconds input history (green), we predict diverse future trajectories (current location in orange, past in red).