Flexible Automatic Football Filming and Summarization

Turchini, Francesco; Seidenari, Lorenzo; Galteri, Leonardo; Ferracani, Andrea; Becchi, Giuseppe; Bimbo, Alberto Del

doi:10.1145/3347318.3355526

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…They produce statistics of events within a game by either analyzing camera shots or semantic information. Human activity localization in sports videos is studied in [192], [193], [194], [195], salient game actions are identified in [196], [197], automatic game highlights identification and summarization are performed in [198], [199], [200], [201], [202]. Moreover, action spotting, which is the task of temporal localization of human-induced events, has been popular in soccer game broadcasts [3], [203] and some methods aimed to automatically detect goals, penalties, corner kicks, and card events [204].…”

Section: Action Spotting In Sportsmentioning

confidence: 99%

Deep Learning-based Action Detection in Untrimmed Videos: A Survey

Tian¹

2021

Preprint

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Understanding human behavior and activity facilitates advancement of numerous real-world applications, and is critical for video analysis. Despite the progress of action recognition algorithms in trimmed videos, the majority of real-world videos are lengthy and untrimmed with sparse segments of interest. The task of temporal activity detection in untrimmed videos aims to localize the temporal boundary of actions and classify the action categories. Temporal activity detection task has been investigated in full and limited supervision settings depending on the availability of action annotations. This paper provides an extensive overview of deep learning-based algorithms to tackle temporal action detection in untrimmed videos with different supervision levels including fully-supervised, weakly-supervised, unsupervised, self-supervised, and semi-supervised. In addition, this paper also reviews advances in spatio-temporal action detection where actions are localized in both temporal and spatial dimensions. Moreover, the commonly used action detection benchmark datasets and evaluation metrics are described, and the performance of the state-of-the-art methods are compared. Finally, real-world applications of temporal action detection in untrimmed videos and a set of future directions are discussed.

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Section: Action Spotting In Sportsmentioning

confidence: 99%

Deep Learning-based Action Detection in Untrimmed Videos: A Survey

Tian¹

2021

Preprint

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“…Computer vision methods have been developed to help understand sport broadcasts, carry out analytics within a game [12,20,66], or even assist in broadcast production. Interesting use cases innclude the automatic summarization of games [21,56,69], the identification of salient game actions [23,45,76] or the reporting of commentaries of live game video streams [78].…”

Section: Related Workmentioning

confidence: 99%

Improved Soccer Action Spotting using both Audio and Video Streams

Vanderplaetse

Dupont

2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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In this paper, we propose a study on multi-modal (audio and video) action spotting and classification in soccer videos. Action spotting and classification are the tasks that consist in finding the temporal anchors of events in a video and determine which event they are. This is an important application of general activity understanding. Here, we propose an experimental study on combining audio and video information at different stages of deep neural network architectures. We used the SoccerNet benchmark dataset, which contains annotated events for 500 soccer game videos from the Big Five European leagues. Through this work, we evaluated several ways to integrate audio stream into video-only-based architectures. We observed an average absolute improvement of the mean Average Precision (mAP) metric of 7.43% for the action classification task and of 4.19% for the action spotting task.

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“…Computer vision tools are widely used in sports broadcast videos to provide soccer analytics [42,57]. Current challenges lie in understanding high-level game information to identify salient game actions [13,60], perform automatic game summarization [49,51,61] or report commentaries of live actions [64].…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Loss Function for Action Spotting in Soccer Videos

Cioppa¹,

Deliège²,

Giancola³

et al. 2019

Preprint

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Action spotting is an important element of general activity understanding. It consists of detecting human-induced events annotated with single timestamps. In this paper, we propose a novel loss function for action spotting. Our loss aims at dealing specifically with the temporal context naturally present around an action. Rather than focusing on the single annotated frame of the action to spot, we consider different temporal segments surrounding it and shape our loss function accordingly. We test our loss on SoccerNet, a large dataset of soccer videos, showing an improvement of 12.8% on the current baseline. We also show the generalization capability of our loss function on ActivityNet for activity proposals and detection, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and identify challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions, and how such semantic knowledge can be leveraged to design a highlights generator.

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Flexible Automatic Football Filming and Summarization

Cited by 7 publications

References 7 publications

Deep Learning-based Action Detection in Untrimmed Videos: A Survey

Deep Learning-based Action Detection in Untrimmed Videos: A Survey

Improved Soccer Action Spotting using both Audio and Video Streams

A Context-Aware Loss Function for Action Spotting in Soccer Videos

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