City-scale mass gatherings attract hundreds of thousands of pedestrians. These pedestrians need to be monitored constantly to detect critical crowd situations at an early stage and to mitigate the risk that situations evolve towards dangerous incidents. Hereby, the crowd density is an important characteristic to assess the criticality of crowd situations. In this work, we consider location-aware smartphones for monitoring crowds during mass gatherings as an alternative to established video-based solutions. We follow a participatory sensing approach in which pedestrians share their locations on a voluntary basis. As participation is voluntarily, we can assume that only a fraction of all pedestrians shares location information. This raises a challenge when concluding about the crowd density. We present a methodology to infer the crowd density even if only a limited set of pedestrians share their locations. Our methodology is based on the assumption that the walking speed of pedestrians depends on the crowd density. By modeling this behavior, we can infer a crowd density estimation. We evaluate our methodology with a real-world data set collected during the Lord Mayor's Show 2011 in London. This festival attracts around half a million spectators and we obtained the locations of 828 pedestrians. With this data set, we first verify that the walking speed of pedestrians depends on the crowd density. In particular, we identify a crowd density-dependent upper limit speed with which pedestrians move through urban spaces. We then evaluate the accuracy of our methodology by comparing our crowd density estimates to ground truth information obtained from video cameras used by the authorities. We achieve an average calibration error of 0.36 m-2 and confirm the appropriateness of our model. With a discussion of the limitations of our methodology, we identify the area of application and conclude that smartphones are a promising tool for crowd monitoring.
JSON, XML-based 3D formats (e.g. X3D or Collada) and Declarative 3D approaches share some benefits but also one major drawback: all encoding schemes store the scene-graph and vertex data in the same file structure; unstructured raw mesh data is found within descriptive elements of the scene. Web Browsers therefore have to download all elements (including every single coordinate) before being able to further process the structure of the document. Therefore, we separate the structured scene information and unstructured vertex data to increase the user experience and overall performance of the system by introducing two new referenced containers, which encode external mesh data as so-called Sequential Image Geometry (SIG) or Typed-Array-based Binary Geometry (BG). We also discuss compression, rendering and application results and introduce a novel data layout for image geometry data that supports incremental updates, arbitrary input meshes and GPU decoding
Gathering representative data using mobile sensing to answer research questions is becoming increasingly popular, driven by growing ubiquity and sensing capabilities of mobile devices. However, there are pitfalls along this path, which introduce heterogeneity in the gathered data, and which are rooted in the diversity of the involved device platforms, hardware, software versions and participants. Thus, we, as a research community, need to establish good practices and methodologies for addressing this issue in order to help ensure that, e.g., scientific results and policy changes based on collective, mobile sensed data are valid. In this paper, we aim to inform researchers and developers about mobile sensing data heterogeneity and ways to combat it. We do so via distilling a vocabulary of underlying causes, and via describing their effects on mobile sensing-building on experiences from three projects within citizen science, crowd awareness and trajectory tracking.
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