Pose estimation is highly valued in surveillance systems in the era of big data. However, current human pose datasets are limited in their coverage of the pose estimation challenges in outdoor surveillance scenarios. In this paper, we introduce a novel Surveillance Human Pose Dataset (SHPD). Unlike the existing fine-grained parts or key-points based human pose datasets, SHPD is built for two aims: 1) constructing a more specialized human pose benchmark for surveillance tasks, and 2) focusing on coarse-grained global-pose estimation for s- mall scale human objects, which are the most common targets in practical outdoor surveillance applications. The collected images in SHPD are all from on-using surveillance cameras and capture people from a wide and balanced range of outdoor scenarios. A wide variety of surveillance human global pos- es and their corresponding rich attributes are also provided. Based on SHPD, performance evaluation of global-pose esti- mation using a few baseline deep-learning networks indicates that, there are ample room for improvement of the recognition accuracy.