用多摄像头实现快速3D目标追踪与姿态估计，无需3D训练数据。
Efficient Online 3D Multi-Camera Multi-Object Tracking and Pose Estimation
- 基于贝叶斯最优滤波，仅需2D检测结果进行在线推断。
- 速度远超现有方法，且在断连场景下仍保持稳定性能。
- 适合部署于真实多相机系统，尤其适用于网络不稳定环境。
本文提出一种高效、在线的多单目相机3D多目标追踪与姿态估计方法。算法仅依赖2D边界框和姿态检测结果，无需昂贵的3D训练数据或计算量大的深度学习模型。该方案是贝叶斯最优多目标追踪滤波器的高效实现，在保证精度的同时大幅提升计算效率。实验表明，仅使用公开预训练的2D检测模型，本方法显著快于当前最优方法，且在多相机运行中出现间歇性断连或重连时仍表现出良好鲁棒性。
原文摘要 · Abstract (English)
This paper proposes a fast and online method for jointly performing 3D multi-object tracking and pose estimation using multiple monocular cameras. Our algorithm requires only 2D bounding box and pose detections, eliminating the need for costly 3D training data or computationally expensive deep learning models. Our solution is an efficient implementation of a Bayes-optimal multi-object tracking filter, enhancing computational efficiency while maintaining accuracy. We demonstrate that our algorithm is significantly faster than state-of-the-art methods without compromising accuracy, using only publicly available pre-trained 2D detection models. We also illustrate the robust performance of our algorithm in scenarios where multiple cameras are intermittently disconnected or reconnected during operation.
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