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In this article, we argue that the energy spent in designing autonomous camera control systems is not spent in vain. We present a real-time virtual camera system that can create smooth camera motion. Similar systems are frequently benchmarked with the human operator as the best possible reference; however, we avoid a priori assumptions in our evaluations. Our main question is simply whether we can design algorithms to steer a virtual camera that can compete with the user experience for recordings from an expert operator with several years of experience? In this respect, we present two low-complexity servoing methods that are explored in two user studies. The results from the user studies give a promising answer to the question pursued. Furthermore, all components of the system meet the real-time requirements on commodity hardware. The growing capabilities of both hardware and network in mobile devices give us hope that this system can be deployed to mobile users in the near future. Moreover, the design of the presented system takes into account that services to concurrent users must be supported. [ABSTRACT FROM AUTHOR]

A pan-tilt-zoom (PTZ) robotic camera can provide a detailed live video of selected areas of interest within a large potential viewing field. The selective coverage is ideal for nature observation applications where power and bandwidth are often limited. To provide the spatial context for human observers, it is desirable to insert the live video into a large spherical panoramic display representing the entire viewing field. Accurate alignment of the video stream within the panoramic display is difficult due to imprecise pan-tilt values and rapid changes in camera configurations. Common image alignment algorithms are computationally expensive for real time applications. We are interested in designing algorithms that fit low power computation platform and hence can be implemented inside the PTZ camera in the future. We present a sampling-based constant-time image alignment algorithm based on spherical projection and projection-invariant selective sampling that accurately registers paired images at 100 frames per second on a simulated embedded platform. The alignment accuracy actually is better than existing methods when high rotational difference is involved. Experiments suggest that the new alignment algorithm is faster than existing algorithms by 1,471.6 times when aligning a six-mega-pixel image pair. [ABSTRACT FROM AUTHOR]