论文标题
多人类3D姿势估计的跨视图跟踪以上超过100 fps
Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
论文作者
论文摘要
实时估算多个人类的3D姿势是计算机视觉中的经典但仍然具有挑战性的任务。它的主要困难在于，当有多个观点中有多个人时，二维姿势的跨观景和巨大状态空间的歧义。在本文中，我们为从多个校准的摄像头视图中介绍了一种新颖的解决方案，以估算多人类3D姿势。它在不同的相机坐标中需要2D姿势作为输入，目的是全局坐标中准确的3D姿势。与以前的方法相关联的2D姿势在每一帧中从头开始的所有视图对，我们利用视频中的时间一致性，将2D输入与3D姿势直接以3空间为单位匹配。更具体地说，我们建议为每个人保留3D姿势，并通过跨视图多人跟踪对其进行迭代更新。正如我们在广泛使用的公共数据集上所证明的那样，这种新颖的表述提高了准确性和效率。为了进一步验证我们方法的可扩展性，我们提出了一个新的大型多人数据集，具有12至28个相机视图。我们的解决方案没有铃铛和哨子，可在12台摄像头上达到154 fps，在28台摄像头上达到34 fps，表明其能够处理大型现实世界应用的能力。提出的数据集在https://github.com/longcw/crossview_3d_pose_tracking上发布。
Estimating 3D poses of multiple humans in real-time is a classic but still challenging task in computer vision. Its major difficulty lies in the ambiguity in cross-view association of 2D poses and the huge state space when there are multiple people in multiple views. In this paper, we present a novel solution for multi-human 3D pose estimation from multiple calibrated camera views. It takes 2D poses in different camera coordinates as inputs and aims for the accurate 3D poses in the global coordinate. Unlike previous methods that associate 2D poses among all pairs of views from scratch at every frame, we exploit the temporal consistency in videos to match the 2D inputs with 3D poses directly in 3-space. More specifically, we propose to retain the 3D pose for each person and update them iteratively via the cross-view multi-human tracking. This novel formulation improves both accuracy and efficiency, as we demonstrated on widely-used public datasets. To further verify the scalability of our method, we propose a new large-scale multi-human dataset with 12 to 28 camera views. Without bells and whistles, our solution achieves 154 FPS on 12 cameras and 34 FPS on 28 cameras, indicating its ability to handle large-scale real-world applications. The proposed dataset is released at https://github.com/longcw/crossview_3d_pose_tracking.
