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While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices.
In this paper, we report on four generations of display-sensor platforms for handheld augmented reality. The paper is organized as a compendium of requirements that guided the design and construction of each generation of the handheld platforms. The first generation, reported in [17]), was a result of various studies on ergonomics and human factors. Thereafter, each following iteration in the design-production process was guided by experiences and evaluations that resulted in new guidelines for future versions. We describe the evolution of hardware for handheld augmented reality, the requirements and guidelines that motivated its construction.
Environment monitoring using multiple observation cameras is increasingly popular. Different techniques exist to visualize the incoming video streams, but only few evaluations are available to find the best suitable one for a given task and context. This article compares three techniques for browsing video feeds from cameras that are located around the user in an unstructured manner. The techniques allow mobile users to gain extra information about the surroundings, the objects and the actors in the environment by observing a site from different perspectives. The techniques relate local and remote cameras topologically, via a tunnel, or via bird's eye viewpoint. Their common goal is to enhance spatial awareness of the viewer, without relying on a model or previous knowledge of the environment. We introduce several factors of spatial awareness inherent to multi-camera systems, and present a comparative evaluation of the proposed techniques with respect to spatial understanding and workload.