Due to the popularity of GPS-equipped cameras such as smartphones, geo-tagged image datasets are widely available and they became a good source to record every corner of urban streets and to understand people's ev...
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ISBN:
(纸本)9781538653210
Due to the popularity of GPS-equipped cameras such as smartphones, geo-tagged image datasets are widely available and they became a good source to record every corner of urban streets and to understand people's everyday life. However, even in the presence of such large visual datasets, a simple question is not yet answered about how much such data cover a certain area spatially. For example, when we have millions of geo-tagged images in San Francisco, how do we know if this dataset visually covers the city completely or not in an intuitive way? Do we have visual coverage of a specific region from all directions or from only a certain direction? This paper provides an answer to such a question by introducing new measurement models to collectively quantify the spatial and directional coverage of a geo-tagged image dataset for a given geographical region. Our experimental results using large real datasets demonstrate that our proposed models are able to well represent the spatial coverage of a geo-tagged image dataset, which is comparable to human perception.
Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field,...
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Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks.
coverage estimation is one of the fundamental problems in sensor networks. coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D (omnidirectional) scalar sensor networks (SS...
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coverage estimation is one of the fundamental problems in sensor networks. coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D (omnidirectional) scalar sensor networks (SSNs) because of the directional sensing nature of cameras and the existence of visual occlusion in crowded environments. This article represents a first attempt toward a closed-form solution for the visual coverage estimation problem in the presence of occlusions. We investigate a new target detection model, referred to as the certainty-based target detection (as compared to the traditional uncertainty-based target detection) to facilitate the formulation of the visual coverage problem. We then derive the closed-form solution for the estimation of the visual coverage probability based on this new target detection model that takes visual occlusions into account, According to the coverage estimation model, we further propose an estimate of the minimum sensor density that suffices to ensure a visual K-coverage in a crowded sensing field. Simulation is conducted which shows extreme consistency with results from theoretical formulation, especially when the boundary effect is considered. Thus, the closed-form solution for visual coverage estimation is effective when applied to real scenarios, such as efficient sensor deployment and optimal sleep scheduling.
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