融合环境先验的双级变压器模型，精准预测网球落地点
A prior information informed learning architecture for flying trajectory prediction
- 用环境先验（如球场边界）与双级变压器结构联合建模轨迹
- 在真实室外球场上实现网球落地点预测，误差显著低于现有方法
- 仅需单个工业相机，硬件要求低，适合实际场景部署
飞行物体轨迹预测在体育分析到航空航天等多个领域至关重要。然而，传统方法在复杂物理建模、计算效率和硬件需求方面存在瓶颈，常忽略关键轨迹事件（如落地点）。本文提出一种新型、硬件高效的轨迹预测框架，将环境先验与双级变压器级联（DTC）架构相结合。通过单个工业相机与基于YOLO的目标检测，提取真实户外球场中网球高速飞行的坐标数据。这些坐标与结构化环境先验（如球场边界）融合，构成完整数据集输入所提出的DTC模型。一级Transformer负责轨迹分类，二级Transformer则综合特征以精确预测落地点。大量消融实验与对比测试表明，在DTC架构中融入环境先验能显著优于现有轨迹预测框架。
原文摘要 · Abstract (English)
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
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