A prior information informed learning architecture for flying trajectory prediction

2026-03-06

AI Summary

This work addresses the limitations of traditional flight trajectory prediction methods, which suffer from complex physical modeling, high computational costs, and difficulty in accurately forecasting critical events such as landing points. To overcome these challenges, the authors propose a Dual Transformer Cascade (DTC) architecture that explicitly integrates environmental structural priors. Leveraging monocular industrial camera data and YOLO-based object detection to obtain projectile coordinates, the model employs scene geometry priors to first perform trajectory semantic classification and then refine the landing point estimation in a second stage. This approach represents the first effort to explicitly embed environmental priors into a trajectory prediction framework. Evaluated in real-world outdoor tennis court scenarios, the method significantly outperforms existing techniques, demonstrating both high efficiency and accuracy in predicting tennis ball landing locations and validating the effectiveness of the proposed architecture and prior-fusion strategy.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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

Problem

• trajectory prediction
• computational inefficiency
• hardware demands
• landing point prediction
• physical modeling

Innovation

• environmental priors
• Dual-Transformer-Cascaded
• trajectory prediction
• landing point estimation
• hardware-efficient learning