# CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

arXiv: https://arxiv.org/abs/2005.12469 · HTML: https://mlsanity.com/paper/2005.12469/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 11 of 11)

- [LG-Traj: LLM Guided Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2403.08032/) (2024)
- [SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2112.02459/) (2021)
- [VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer](https://mlsanity.com/paper/2311.06623/) (2023)
- [Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems](https://mlsanity.com/paper/2210.08057/) (2022)
- [CCF: Cross Correcting Framework for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2406.00749/) (2024)
- [Remember and Recall: Associative-Memory-based Trajectory Prediction](https://mlsanity.com/paper/2410.02201/) (2024)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints](https://mlsanity.com/paper/2309.17338/) (2023)
- [STF: Spatial Temporal Fusion for Trajectory Prediction](https://mlsanity.com/paper/2311.18149/) (2023)
- [Spatial-Temporal Conditional Random Field for Human Trajectory Prediction](https://mlsanity.com/paper/2311.18198/) (2023)
- [Enhancing Trajectory Prediction through Self-Supervised Waypoint Noise Prediction](https://mlsanity.com/paper/2312.09466/) (2023)

## Abstract (arXiv)

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.