Résumé: The potential of video surveillance can be further explored by using mobile cameras. Drone-mounted cameras at a high altitude can provide top views of a scene from a global perspective while cameras worn by people on the ground can provide first-person views of the same scene with more local details. To relate these two views for collaborative analysis, we propose to localize the field of view of the first-person-view cameras in the global top view. This is a very challenging problem due to their large view differences and indeterminate camera motions. In this work, we explore the use of sunlight direction as a bridge to relate the two views. Specifically, we design a shadow-direction-aware network to simultaneously locate the shadow vanishing point in the first-person view as well as the shadow direction in the top view. Then we apply multi-view geometry to estimate the yaw and pitch angles of the first-person-view camera in the top view. We build a new synthetic dataset consisting of top-view and first-person-view image pairs for performance evaluation. Quantitative results on this synthetic dataset show the superiority of our method compared with the existing methods, which achieve the view angle estimation errors of 1.61 (pitch angle) and 15.13 (yaw angle), respectively. The qualitative results on real images also show the effectiveness of the proposed method.

Langue: Anglais