We introduce view birdification, the problem of recovering ground-plane movements of people in a crowd from an ego-centric video captured from an observer (e.g., a person or a vehicle) also moving in the crowd. Recovered ground-plane movements would provide a sound basis for situational understanding and benefit downstream applications in computer vision and robotics. In this paper, we formulate view birdification as a geometric trajectory reconstruction problem and derive a cascaded optimization method from a Bayesian perspective. The method first estimates the observer's movement and then localizes surrounding pedestrians for each frame while taking into account the local interactions between them. We introduce three datasets by leveraging synthetic and real trajectories of people in crowds and evaluate the effectiveness of our method. The results demonstrate the accuracy of our method and set the ground for further studies of view birdification as an important but challenging visual understanding problem.
翻译:我们引入了观景鸟类化问题,即从从观察者(例如一人或一辆车)同时也在人群中移动的以自我为中心的视频中从人群中获取的人群中恢复地面飞机移动的问题; 被回收的地面飞机移动将为了解情况并有益于计算机视觉和机器人的下游应用提供一个良好的基础; 在本文中,我们将鸟类化视为几何轨道重建问题,并从巴伊西亚的角度得出一个级联优化方法; 这种方法首先估计观察员的移动,然后将每个框架周围行人本地化,同时考虑到他们之间的当地互动; 我们通过利用人群中的合成和真实轨迹引入三个数据集,并评估我们方法的有效性; 结果表明我们的方法的准确性,并为进一步研究鸟类化作为一个重要但具有挑战性的视觉理解问题奠定了基础。