Abstract
Although the growth of video content in online platforms has been happening for some time, searching and browsing these assets is still very inefficient as rich contextual data that describes the content is still not available. Furthermore, any available descriptions are, usually, not linked to timed moments of content. In this paper, we present an approach for making social web videos available on YouTube more accessible, searchable and navigable. By using the concept of crowdsourcing to collect the metadata, our proposal can contribute to easily enhance content uploaded in the YouTube platform. Metadata, collected as a collaborative annotation game, is added to the content as time-based information in the form of descriptions and captions using the YouTube API. This contributes for enriching video content and enabling navigation through temporal links. © Springer Nature Switzerland AG 2019.

Abstract

Abstract

Abstract
The growing importance of Radar solutions in automotive applications results in new standards to be met and more demanding performances to be assured. As such, Radar developers require testers capable of guaranteeing compliance with the specified criteria. This paper presents a preliminary approach to allow over-the-air (OTA) testing and validation of 76-77 GHz and 77-81 GHz Radar units in production lines, covering not only pass/fail conditions, but also determination of the Radar antenna array radiation diagrams (in azimuth and elevation) through a calibrated external power sensor. Preliminary measurements on an open environment test bench are shown for different Radar bandwidths (1 GHz and 4 GHz), illustrating the importance of a clean and shielded anechoic chamber environment as a baseline approach. © 2019 IEEE.

Abstract
The development of efficient methods for searching and browsing large assets of video content has been considered by the academia and content owners for long. Different approaches that range from manual structured annotations, to unstructured metadata collected from several sources, as well as multimedia processing for automatic description of the content, can be identified. The growth on the number of hours of video content put online in video sharing platforms has however shown that video retrieval is still quite inefficient as rich contextual data that describes the content is most of the times still not available. Additionally, metadata is usually not linked to timed moments of content, making direct access to the most relevant moments not possible. In this paper, an approach for making web videos available in the YouTube platform more accessible is presented. The solution is based on a collaborative process presented as a game that enables collecting metadata from the crowd while implementing mechanisms that remove erroneous information usually encountered in this type of information. Metadata, exported to YouTube in the form of captions and descriptions, contributes to enhance video retrieval, guaranteeing a better user experience and exposure of the content.