Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones, and glasses, we have access to a plethora of videos captured from first person perspective. Surveillance cameras and Unmanned Aerial Vehicles (UAVs) also offer tremendous amounts of video data recorded from top and oblique view points. Egocentric and surveillance vision have been studied extensively but separately in the computer vision community. The relationship between these two domains, however, remains unexplored. In this study, we make the first attempt in this direction by addressing two basic yet challenging questions. First, having a set of egocentric videos and a top-view surveillance video, does the top-view video contain all or some of the egocentric viewers? In other words, have these videos been shot in the same environment at the same time? Second, if so, can we identify the egocentric viewers in the top-view video? These problems can become extremely challenging when videos are not temporally aligned. Each view, egocentric or top, is modeled by a graph and the assignment and time-delays are computed iteratively using the spectral graph matching framework. We evaluate our method in terms of ranking and assigning egocentric viewers to identities present in the top-view video over a dataset of 50 top-view and 188 egocentric videos captured under different conditions. We also evaluate the capability of our proposed approaches in terms of temporal alignment. The experiments and results demonstrate the capability of the proposed approaches in terms of jointly addressing the temporal alignment and assignment tasks.

中文翻译：

---

自我中心与顶视图

由于可穿戴设备（例如GoPro相机，智能手机和眼镜）的可用性和日益普及，我们可以访问从第一人称视角捕获的大量视频。监视摄像机和无人机（UAV）还提供了从俯视和倾斜视点记录的大量视频数据。以自我为中心和监视视觉已在计算机视觉社区中被广泛研究，但分别进行了研究。但是，这两个域之间的关系尚待探索。在这项研究中，我们通过解决两个基本但具有挑战性的问题来朝这个方向进行了首次尝试。首先，拥有一组以自我为中心的视频和一个顶视监视视频，该顶视视频是否包含全部或部分以自我为中心的观看者？换句话说，这些视频是同时在同一环境中拍摄的吗？其次，如果是这样，我们能否在顶视视频中识别以自我为中心的观看者？当视频未在时间上对齐时，这些问题将变得极具挑战性。每个视图（以自我为中心或顶部）均由图形建模，并且使用光谱图匹配框架迭代地计算分配和时间延迟。我们通过对在不同条件下捕获的50个顶视图视频和188个以自我为中心的视频的数据集进行排名并将以自我为中心的观看者分配给顶视图视频中存在的身份的方式来评估我们的方法。我们还根据时间对齐方式评估了我们提出的方法的能力。实验和结果证明了所提出的方法在共同解决时间对准和分配任务方面的能力。