Multimodal framework based on audio‐visual features for summarisation of cricket videos

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166171/1/ipr2bf02094.pd

Goal event detection in soccer videos via collaborative multimodal analysis

Detecting semantic events in sports video is crucial for video indexing and retrieval. Most existing works have exclusively relied on video content features, namely, directly available and extractable data from the visual and/or aural channels. Sole reliance on such data however, can be problematic due to the high-level semantic nature of video and the difficulty to properly align detected events with their exact time of occurrences. This paper proposes a framework for soccer goal event detection through collaborative analysis of multimodal features. Unlike previous approaches, the visual and aural contents are not directly scrutinized. Instead, an external textual source (i.e., minute-by-minute reports from sports websites) is used to initially localize the event search space. This step is vital as the event search space can significantly be reduced. This also makes further visual and aural analysis more efficient since excessive and unnecessary non-eventful segments are discarded, culminating in the accurate identification of the actual goal event segment. Experiments conducted on thirteen soccer matches are very promising with high accuracy rates being reported

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Soccer on Social Media

In the era of digitalization, social media has become an integral part of our lives, serving as a significant hub for individuals and businesses to share information, communicate, and engage. This is also the case for professional sports, where leagues, clubs and players are using social media to reach out to their fans. In this respect, a huge amount of time is spent curating multimedia content for various social media platforms and their target users. With the emergence of Artificial Intelligence (AI), AI-based tools for automating content generation and enhancing user experiences on social media have become widely popular. However, to effectively utilize such tools, it is imperative to comprehend the demographics and preferences of users on different platforms, understand how content providers post information in these channels, and how different types of multimedia are consumed by audiences. This report presents an analysis of social media platforms, in terms of demographics, supported multimedia modalities, and distinct features and specifications for different modalities, followed by a comparative case study of select European soccer leagues and teams, in terms of their social media practices. Through this analysis, we demonstrate that social media, while being very important for and widely used by supporters from all ages, also requires a fine-tuned effort on the part of soccer professionals, in order to elevate fan experiences and foster engagement

Automatic Summarization of Soccer Highlights Using Audio-visual Descriptors

Automatic summarization generation of sports video content has been object of great interest for many years. Although semantic descriptions techniques have been proposed, many of the approaches still rely on low-level video descriptors that render quite limited results due to the complexity of the problem and to the low capability of the descriptors to represent semantic content. In this paper, a new approach for automatic highlights summarization generation of soccer videos using audio-visual descriptors is presented. The approach is based on the segmentation of the video sequence into shots that will be further analyzed to determine its relevance and interest. Of special interest in the approach is the use of the audio information that provides additional robustness to the overall performance of the summarization system. For every video shot a set of low and mid level audio-visual descriptors are computed and lately adequately combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. A variety of results are presented with real soccer video sequences that prove the validity of the approach