从高保真度测量的行人定向动态
Pedestrian orientation dynamics from high-fidelity measurements
摘要：
我们调查在现实生活中的条件和非常高的准确性的身体旋转，或偏航，步行行人的动态-一个高度复杂的任务，由于各种各样的形状，姿势和行走姿势。我们提出了一种基于深度神经架构的新型测量方法，我们在行人运动的通用物理特性的基础上进行训练。具体来说，我们利用个人速度和身体取向之间的强统计相关性：速度方向通常是相对于肩线正交。我们作出合理的假设，即这种近似，虽然瞬时略有不完美，平均是正确的。这使我们能够使用速度数据作为训练标签，用于个体方向的高精度点估计器，我们可以训练而无需专门的注释劳动。我们讨论了测量精度，并显示了误差缩放，无论是在合成和现实生活中的数据：我们表明，我们的方法是能够估计方向的误差低至7.5度。该工具开辟了新的可能性，在人类群体动力学的研究方向是关键。通过分析在现实生活中的条件下，身体旋转的动力学，我们表明，瞬时速度方向可以描述的方向和随机延迟的组合，其中随机性是由一个Ornstein-Uhlenbeck过程集中在100毫秒的平均延迟。量化这些动态可能是唯一可能的，由于一个工具，作为精确的建议。
英文摘要：
We investigate in real-life conditions and with very high accuracy the dynamics of body rotation, or yawing, of walking pedestrians - an highly complex task due to the wide variety in shapes, postures and walking gestures. We propose a novel measurement method based on a deep neural architecture that we train on the basis of generic physical properties of the motion of pedestrians. Specifically, we leverage on the strong statistical correlation between individual velocity and body orientation: the velocity direction is typically orthogonal with respect to the shoulder line. We make the reasonable assumption that this approximation, although instantaneously slightly imperfect, is correct on average. This enables us to use velocity data as training labels for a highly-accurate point-estimator of individual orientation, that we can train with no dedicated annotation labor. We discuss the measurement accuracy and show the error scaling, both on synthetic and real-life data: we show that our method is capable of estimating orientation with an error as low as 7.5 degrees. This tool opens up new possibilities in the studies of human crowd dynamics where orientation is key. By analyzing the dynamics of body rotation in real-life conditions, we show that the instantaneous velocity direction can be described by the combination of orientation and a random delay, where randomness is provided by an Ornstein-Uhlenbeck process centered on an average delay of 100ms. Quantifying these dynamics could have only been possible thanks to a tool as precise as that proposed.
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