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Monitoring the number of people in various spaces of a building is important for optimizing space usage, assisting with public safety, and saving energy. Diverse approaches have been developed for different end goals, from ID card readers for space management, to surveillance cameras for security, to CO2 sensing for HVAC control. In the last few years, fisheye cameras mounted overhead have become the sensing modality of choice because they offer large-area coverage and significantly-reduced occlusions but research efforts are still nascent. In this paper, we provide an overview of recent research efforts in this area and propose one new direction. First, we identify benefits and challenges related to inference from top-view fisheye images, and summarize key public datasets. Then, we review efforts in algorithm development for detecting people from a single fisheye frame and from a group of sequential frames. Finally, we focus on counting people indoors. While this is straightforward for a single camera, when multiple cameras are used to monitor a space, person re-identification is needed to avoid overcounting. We describe a framework for people counting using two cameras and demonstrate its effectiveness in a large classroom for location-based person re-identification. To support people counting in even larger spaces, we propose two new person re-identification algorithms using N > 2 overhead fisheye cameras. We provide ample experimental results throughout the paper.

Road monitoring plays a vital role in emergency disposal, traffic accident liability determination, traffic trend analyzes, and other fields. This paper designs a real-time virtual-real fusion framework for multiview surveillance of large-scale scene, in which 2D panorama, satellite texture, and 3D model are merged. The system is implemented with the following contributions. First, a novel automatic mosaic algorithm for multiview overlapped images is proposed. Feature points between adjacent cameras are detected via comotion statistics map. In addition, transformation model is obtained using random sample consensus algorithm. Second, multiview images are morphed to a same overhead viewpoint. Finally, 2D panoramic road image and 3D scene model are combined based on high-precision ground control points. The proposed framework has been successfully applied to a large road intersection. With the virtual-real fusion approach, observers can freely monitor and ramble in the large-scale 3-D road scene. [ABSTRACT FROM PUBLISHER]