Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades, and the current state of the art is represented by methods that rely on modern deep neural network architectures. This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization. After presenting the motivation behind the development of technologies for video summarization, we formulate the video summarization task and discuss the main characteristics of a typical deep-learning-based analysis pipeline. Then, we suggest a taxonomy of the existing algorithms and provide a systematic review of the relevant literature that shows the evolution of the deep-learning-based video summarization technologies and leads to suggestions for future developments. We then report on protocols for the objective evaluation of video summarization algorithms, and we compare the performance of several deep-learning-based approaches. Based on the outcomes of these comparisons, as well as some documented considerations about the amount of annotated data and the suitability of evaluation protocols, we indicate potential future research directions.

Video Summarization Using Deep Neural Networks: A Survey

In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot. We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We show the generalization capability of our loss for generic activity proposals and detection on ActivityNet, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and display challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions and show how such semantic knowledge can be used for automatic highlights generation.

/pdf/a-context-aware-loss-function-for-action-spotting-in-soccer-22pvq1yvm9.pdf

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

Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNetv2, and achieve new state-of-the-art performances.

/pdf/camera-calibration-and-player-localization-in-soccernet-v2-1j49d8vyv2.pdf

Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

Soccer videos are a rich playground for computer vision, involving many elements, such as players, lines, and specific objects. Hence, to capture the richness of this sport and allow for fine automated analyses, we release SoccerNet-v3, a major extension of the SoccerNet dataset, providing a wide variety of spatial annotations and cross-view correspondences. SoccerNet's broadcast videos contain replays of important actions, allowing us to retrieve a same action from different viewpoints. We annotate those live and replay action frames showing same moments with exhaustive local information. Specifically, we label lines, goal parts, players, referees, teams, salient objects, jersey numbers, and we establish player correspondences between the views. This yields 1,324,732 annotations on 33,986 soccer images, making SoccerNet-v3 the largest dataset for multi-view soccer analysis. Derived tasks may benefit from these annotations, like camera calibration, player localization, team discrimination and multi-view re-identification, which can further sustain practical applications in augmented reality and soccer analytics. Finally, we provide Python codes to easily download our data and access our annotations. 

/pdf/scaling-up-soccernet-with-multi-view-spatial-localization-24yih10h.pdf

Scaling up SoccerNet with multi-view spatial localization and re-identification

Traditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exploration scenarios. Multi-modal video summarization is one of the methods utilized to address this problem. When multi-modal video summarization is used to help video exploration, a text-based query is considered as one of the main drivers of video summary generation, as it is user-defined. Thus, encoding both the text-based query and the video effectively is important for the task of multi-modal video summarization. In this work, a new method is proposed that uses a specialized attention network and contextualized word representations to tackle this task. The proposed model consists of a contextualized video summary controller, multi-modal attention mechanisms, an interactive attention network, and a video summary generator. Based on the evaluation of the existing multi-modal video summarization benchmark, experimental results show that the proposed model is effective with the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared with the state-of-the-art method. https://github.com/Jhhuangkay/GPT2MVS-Generative-Pre-trained-Transformer-2-for-Multi-modal-Video-Summarization.

pdf/gpt2mvs-generative-pre-trained-transformer-2-for-multi-modal-2ucbls8zpv.pdf

GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization

The massive growth of sports videos, specially in soccer, has resulted in a need for the automatic generation of summaries, where the objective is not only to show the most important actions of the match but also to elicit as much emotion as the ones bring upon by human editors. State-of-the-art methods on video summarization mostly rely on video processing, however this is not an optimal approach for long videos such as soccer matches. In this paper we propose a multimodal approach to automatically generate summaries of soccer match videos that consider both event and audio features. The event features get a shorter and better representation of the match, and the audio helps detect the excitement generated by the game. Our method consists of three consecutive stages: Proposals, Summarization and Content Refinement. The first one generates summary proposals, using Multiple Instance Learning to deal with the similarity between the events inside the summary and the rest of the match. The Summarization stage uses event and audio features as input of a hierarchical Recurrent Neural Network to decide which proposals should indeed be in the summary. And the last stage, takes advantage of the visual content to create the final summary. The results show that our approach outperforms by a large margin not only the video processing methods but also methods that use event and audio features.

A Deep Architecture for Multimodal Summarization of Soccer Games

—Video content is present in an ever-increasing num- ber of ﬁelds, both scientiﬁc and commercial. Sports, particularly soccer, is one of the industries that has invested the most in the ﬁeld of video analytics, due to the massive popularity of the game and the emergence of new markets (such as sport betting markets). Previous state-of-the-art methods on soccer matches video summarization rely on handcrafted heuristics to generate summaries which is poorly generalizable, but these works have yet proven that multiple modalities help detect the best actions of the game. On the other hand, machine learning models with higher generalization potential have enter the ﬁeld of summarization of general-purpose videos, offering several deep learning approaches. However, most of them exploit content speciﬁcities that are not appropriate for sport whole-match videos. Although video content has been for many years the main source for automatizing knowledge extraction in soccer, the data that records all the events happening on the ﬁeld has become lately very important in sports analytics, since this event data provides richer context information and requires less processing. Considering that in automatic sports summarization, the goal is not only to show the most important actions of the game, but also to reproduce the storytelling of the whole match with as much emotion as the one evoked by human editors, we propose a method to generate the summary of a soccer match video exploiting both the audio and the event metadata of the entire match. The results show that our method can detect the actions of the match, identify which of these actions should belong to the summary and then propose multiple candidate summaries which are similar enough but with relevant variability to provide different options to the ﬁnal editor. Furthermore, we show the generalization capability of our work since it can transfer knowledge between datasets from different broadcasting companies, from different competitions, acquired in different conditions, and corresponding to summaries of different lengths.

A Multi-stage deep architecture for summary generation of soccer videos

Currently, in broadcast companies many human operators select which actions should belong to the summary based on multiple rules they have built upon their own experience using different sources of information. These rules define the different profiles of actions of interest that help the operator to generate better customized summaries. Most of these profiles do not directly rely on broadcast video content but rather exploit metadata describing the course of the match. In this paper, we show how the signals produced by the attention layer of a recurrent neural network can be seen as a learned representation of these action profiles and provide a new tool to support operators’ work. The results in soccer matches show the capacity of our approach to transfer knowledge between datasets from different broadcasting companies, from different leagues, and the ability of the attention layer to learn meaningful action profiles.

/pdf/profiling-actions-for-sport-video-summarization-an-attention-5g4tcyffok.pdf

Profiling Actions for Sport Video Summarization: An attention signal analysis

The way people consume sports on TV has drastically evolved in the last years, particularly under the combined effects of the legalization of sport betting and the huge increase of sport analytics. Several companies are nowadays sending observers in the stadiums to collect live data of all the events happening on the field during the match. Those data contain meaningful information providing a very detailed description of all the actions occurring during the match to feed the coaches and staff, the fans, the viewers, and the gamblers. Exploiting all these data, sport broadcasters want to generate extra content such as match highlights, match summaries, players and teams analytics, etc., to appeal subscribers. This paper explores the problem of summarizing professional soccer matches as automatically as possible using both the aforementioned event-stream data collected from the field and the content broadcasted on TV. We have designed an architecture, introducing first (1) a Multiple Instance Learning method that takes into account the sequential dependency among events and then (2) a hierarchical multimodal attention layer that grasps the importance of each event in an action. We evaluate our approach on matches from two professional European soccer leagues, showing its capability to identify the best actions for automatic summarization by comparing with real summaries made by human operators.

/pdf/hierarchical-multimodal-attention-for-deep-video-4i6tgeju3i.pdf

Hierarchical Multimodal Attention for Deep Video Summarization

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A Deep Architecture for Multimodal Summarization of Soccer Games

A Multi-stage deep architecture for summary generation of soccer videos

Profiling Actions for Sport Video Summarization: An attention signal analysis

Hierarchical Multimodal Attention for Deep Video Summarization