# A prior information informed learning architecture for flying trajectory prediction

arXiv: https://arxiv.org/abs/2603.06863 · HTML: https://mlsanity.com/paper/2603.06863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction](https://mlsanity.com/paper/2312.06704/) (2023)
- [Real Time Trajectory Prediction Using Deep Conditional Generative Models](https://mlsanity.com/paper/1909.03895/) (2019)
- [Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer](https://mlsanity.com/paper/2307.03786/) (2023)
- [Obstacle-Transformer: A Trajectory Prediction Network Based on Surrounding Trajectories](https://mlsanity.com/paper/2304.07711/) (2023)
- [Exploiting Motion Prior for Accurate Pose Estimation of Dashboard Cameras](https://mlsanity.com/paper/2409.18673/) (2024)

## Abstract (arXiv)

Trajectory prediction for flying objects is critical in domains ranging from sports analytics to aerospace. However, traditional methods struggle with complex physical modeling, computational inefficiencies, and high hardware demands, often neglecting critical trajectory events like landing points. This paper introduces a novel, hardware-efficient trajectory prediction framework that integrates environmental priors with a Dual-Transformer-Cascaded (DTC) architecture. We demonstrate this approach by predicting the landing points of tennis balls in real-world outdoor courts. Using a single industrial camera and YOLO-based detection, we extract high-speed flight coordinates. These coordinates, fused with structural environmental priors (e.g., court boundaries), form a comprehensive dataset fed into our proposed DTC model. A first-level Transformer classifies the trajectory, while a second-level Transformer synthesizes these features to precisely predict the landing point. Extensive ablation and comparative experiments demonstrate that integrating environmental priors within the DTC architecture significantly outperforms existing trajectory prediction frameworks