@inproceedings {42447,
	title = {Automatic Thumbnail Selection for Soccer Videos using Machine Learning},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSys {\textquoteright}22)},
	year = {2022},
	pages = {73-85},
	publisher = {ACM},
	doi = {10.1145/3524273.3528182},
	author = {Husa, Andreas and Midoglu, Cise and Hammou, Malek and Hicks, Steven and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {28442,
	title = {MMSys{\textquoteright}22 Grand Challenge on AI-based Video Production for Soccer},
	year = {2022},
	publisher = {arXiv},
	url = {https://arxiv.org/abs/2202.01031},
	author = {Midoglu, Cise and Hicks, Steven and Thambawita, Vajira and Kupka, Tomas and Halvorsen, P{\r a}l}
}
@article {28313,
	title = {AI-Based Video Clipping of Soccer Events},
	journal = {Machine Learning and Knowledge Extraction},
	volume = {3},
	year = {2021},
	month = {12/2021},
	pages = {990 - 1008},
	publisher = {MDPI},
	abstract = {The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process.\&nbsp;\&nbsp;},
	doi = {10.3390/make3040049},
	url = {https://www.mdpi.com/2504-4990/3/4/49/pdf},
	author = {Joakim Olav {Valand} and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28315,
	title = {Automated Clipping of Soccer Events using Machine Learning},
	journal = {IEEE International Symphosium of Multimedia (ISM)},
	year = {2021},
	month = {12/2021},
	publisher = { IEEE},
	doi = {10.1109/ISM52913.2021.00042},
	author = {Valand, Joakim and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27333,
	title = {PMData: a sports logging dataset},
	journal = {Proceedings of the 11th ACM Multimedia Systems Conference},
	year = {2020},
	pages = {231-236},
	publisher = {ACM},
	author = {Thambawita, Vajira and Hicks, Steven and Borgli, Hanna and H{\r a}kon Kvale {Stensland} and Jha, Debesh and Martin Kristoffer {Svensen} and Pettersen, Svein-Arne and Johansen, Dag and H{\r a}vard Dagenborg {Johansen} and Susann Dahl {Pettersen} and Nordvang, Simon and Pedersen, Sigurd and Gjerdrum, Anders and Gr{\o}nli, Tor-Morten and Per Morten {Fredriksen} and Eg, Ragnhild and Hansen, Kjeld and Fagernes, Siri and Claudi, Christine and Bi{\o}rn-Hansen, Andreas and Duc Tien Dang {Nguyen} and Kupka, Tomas and Hugo Lewi {Hammer} and Jain, Ramesh and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27612,
	title = {Scalable Infrastructure for Efficient Real-Time Sports Analytics},
	journal = {Companion Publication of the 2020 International Conference on Multimodal Interaction},
	year = {2020},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Recent technological advances are adapted in sports to improve performance, avoid injuries, and make advantageous decisions. In this paper, we describe our ongoing efforts to develop and deploy PMSys, our smartphone-based athlete monitoring and reporting system. We describe our first attempts to gain insight into some of the data we have collected. Experiences so far are promising, both on the technical side and for athlete performance development. Our initial application of artificial-intelligence methods for prediction is encouraging and indicative.},
	keywords = {algorithmic analysis, artificial intelligence, Machine learning, privacy-preserving data collection, Sports performance logging},
	isbn = {9781450380027},
	doi = {10.1145/339503510.1145/3395035.3425300},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3395035https://dl.acm.org/doi/10.1145/3395035.3425300https://dl.acm.org/doi/pdf/10.1145/3395035.3425300},
	author = {H{\r a}vard D. {Johansen} and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Truong, Khiet and Heylen, Dirk and Czerwinski, Mary and Berthouze, Nadia and Chetouani, Mohamed and Nakano, Mikio}
}
@article {26631,
	title = {Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {International Journal of Semantic Computing},
	volume = {13},
	number = {3},
	year = {2019},
	pages = {367-391},
	publisher = {World Scientific},
	abstract = {360\&nbsp;panorama video displayed through Virtual reality (VR) glasses or large screens o{\textregistered}ers immersive user experiences, but as such technology becomes commonplace, the need for e{\textpm}cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di{\textregistered}erent trade-o{\textregistered}s in order to save bandwidth while preserving the video quality in the user{\textquoteright}s ̄eld-of-view (FoV). Using 360\&nbsp;VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e{\textpm}cient end-to-end design and real-world implementation of such a 360\&nbsp;streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a\&nbsp;live\&nbsp;streaming system which strikes a trade-o{\textregistered} between bandwidth usage and the video quality in the user{\textquoteright}s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o{\textregistered}s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Johansen, Dag and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26630,
	title = {Predicting Peek Readiness-to-Train of Soccer Players Using Long Short-Term Memory Recurrent Neural Networks},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = {IEEE},
	abstract = {We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine- learning methods have the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams.This paper tackles the problem of deriving peaks in soccer players{\textquoteright} ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90\%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries.},
	doi = {10.1109/CBMI.2019.8877406},
	author = {Wiik, Theodor and H{\r a}vard D {Johansen} and Svein Arne {Pettersen} and Baptista, Ivan and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {25795,
	title = {Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches},
	howpublished = { IEEE Conference on Biomedical and Health Informatics (BHI) 2018},
	year = {2018},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. In this paper, we present several machine-learning-based approaches for angiectasia detection in wireless video capsule endoscopy images. The most promising results for pixel-wise localization and frame-wise detection are obtained by the proposed deep learning approach using generative adversarial networks (GANs) with a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection, which fits the requirements for automatic angiectasia detection in real clinical settings.},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and Espeland, H{\r a}vard and Kupka, Tomas and de Lange, Thomas and Griwodz, Carsten and Riegler, Michael}
}
@inbook {25869,
	title = {Camera Synchronization for Panoramic Videos},
	booktitle = {MediaSync},
	year = {2018},
	month = {03/2018},
	pages = {565-592},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-65840-7_20},
	url = {https://doi.org/10.1007/978-3-319-65840-7_20},
	author = {Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Kupka, Tomas and Espeland, H{\r a}vard and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l},
	editor = {Montagud, Mario and Cesar, Pablo and Boronat, Fernando and Jansen, Jack}
}
@inproceedings {25794,
	title = {Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia},
	journal = {2018 IEEE Conference on Biomedical and Health Informatics (BHI)},
	year = {2018},
	pages = {365-368},
	publisher = {IEEE},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos or images of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. This shows that automatic detection to support medical experts can be useful. In this paper, we present several machine learning-based approaches for angiectasia detection in wireless video capsule endoscopy frames. In summary, the most promising results for pixel-wise localization and framewise detection are obtained by the proposed deep learning method using generative adversarial networks (GANs). Using this approach, we achieve a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization, and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection. Thus, the results demonstrate the capability of using deep learning for automatic angiectasia detection in real clinical settings.\&nbsp;},
	keywords = {Angiectasia, computer aided diagnosis, deep learning, Machine learning, video capsular endoscopy},
	doi = {10.1109/BHI.2018.8333444},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and de Lange, Thomas and Espeland, H{\r a}vard and Kupka, Tomas and Griwodz, Carsten and Riegler, Michael}
}
@inproceedings {26250,
	title = {Efficient Live and on-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {2018 IEEE International Symposium on Multimedia (ISM)},
	year = {2018},
	month = {12/2018},
	pages = {81-88},
	publisher = {IEEE},
	address = {Taichung, Taiwan},
	abstract = {With 360{\textopenbullet} panorama video technology becoming commonplace, the need for efficient streaming methods for such videos arises. We go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user{\textquoteright}s field-of-view. We have created an architecture that combines RTP and DASH to deliver 360{\textopenbullet} VR content to a Huawei set-top-box and a Samsung Galaxy S7. Our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the field-of- view changes.\&nbsp;},
	doi = {10.1109/ISM.2018.00022},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Pogorelov, Konstantin and Riegler, Michael and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@article {19142,
	title = {Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations},
	journal = {Advances in Multimedia},
	volume = {2014},
	year = {2014},
	pages = {1{\textendash}21},
	publisher = {Hindawi},
	issn = {1687-5680},
	doi = {10.1155/2014/805852},
	url = {http://www.hindawi.com/journals/am/2014/805852/},
	author = {Kupka, Tomas and Riiser, Haakon and Ni, Pengpeng and Eg, Ragnhild and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {Simula.simula.2050,
	title = {Analysis of a Real-World HTTP Segment Streaming Case},
	journal = {Proceedings of the European Interactive TV Conference (EuroITV 2013)},
	year = {2013},
	month = {June},
	pages = {75-84},
	publisher = {ACM},
	type = {Conference},
	abstract = {Today, adaptive HTTP segment streaming is a popular way to deliver video content to users. The benefits of HTTP segment streaming include its scalability, high performance and easy deployment, especially the possibility to reuse the already deployed HTTP infrastructure. However, current research focuses merely on client side statistics like for example achieved video qualities and adaption algorithms. To quantify the properties of such streaming systems from a service provider point of view, we have analyzed both sender and receiver side logging data provided by a popular Norwegian streaming provider. For example, we observe that more than 90\% of live streaming clients send their requests for the same video segment with an inter-arrival time of only 10 seconds. Moreover, the logs indicate that the server sends substantially less data than is actually reported to be received by the clients, and the origin server streams data to less clients than there really are. Based on these facts, we conclude that HTTP segment streaming really makes use of the HTTP cache infrastructure without the need to change anything in the parts of Internet that are not controlled by the streaming provider.},
	author = {Kupka, Tomas and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag and Hovden, Torgeir},
	editor = {George Lekakos Pa {Cremonesi}}
}
@phdthesis {Simula.simula.2560,
	title = {On the HTTP Segment Streaming Potentials and Performance Improvements},
	year = {2013},
	month = {February},
	school = {University of Oslo},
	type = {phd},
	author = {Kupka, Tomas}
}
@inproceedings {Simula.simula.1658,
	title = {Performance of On-Off Traffic Stemming From Live Adaptive Segmented HTTP Video Streaming},
	journal = {Proceedings of the IEEE Conference on Local Computer Networks (LCN)},
	year = {2012},
	pages = {405-413},
	publisher = {IEEE},
	type = {Conference},
	abstract = {Nowadays, several live and on-demand streaming solutions use HTTP for signaling and data delivery. A frequently used technique is to chop a continuous stream into segments, encode these in multiple qualities and make these available for download using plain HTTP methods. This approach has become known as dynamic adaptive segment streaming over HTTP. Its advantage is that the deployed web infrastructure is easily reused, even for live segment streaming. In this case, however, it is not strictly bulk traffic. We show in this paper, that the streaming source is essentially an on-off source. Furthermore, this paper analyzes several client-controlled segment request strategies for live adaptive HTTP segment streaming. We present experimental results showing the benefits and drawbacks of each strategy with respect to achieved video quality, smoothness of playback and end-to-end delay. We show that it matters how clients request segments. The results indicate strongly that synchronization of client requests has a negative impact on router queues and leads to increased packet loss, and should thus be avoided to achieve a high goodput.},
	keywords = {Conference},
	doi = {10.1109/LCN.2012.6423654},
	author = {Kupka, Tomas and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {Simula.simula.758,
	title = {An Evaluation of Live Adaptive HTTP Segment Streaming Request Strategies},
	journal = {Proceedings of the IEEE Conference on Local Computer Networks (LCN)},
	year = {2011},
	month = {October},
	pages = {608-616},
	publisher = {IEEE},
	type = {Conference},
	abstract = {Nowadays, several live and on-demand streaming solutions use HTTP for signaling and data delivery. A frequently used technique is to chop a continuous stream into segments, encode these in multiple qualities and make these available for download using plain HTTP methods. This approach has become known as dynamic adaptive segment streaming over HTTP. Its advantage is that the deployed web infrastructure is easily reused, even for live segment streaming. In this case, however, it is not strictly bulk traffic. We show in this paper, that the streaming source is essentially an on-off source. Furthermore, this paper analyzes several client-controlled segment request strategies for live adaptive HTTP segment streaming. We present experimental results showing the benefits and drawbacks of each strategy with respect to achieved video quality, smoothness of playback and end-to-end delay. We show that it matters how clients request segments. The results indicate strongly that synchronization of client requests has a negative impact on router queues and leads to increased packet loss, and should thus be avoided to achieve a high goodput.},
	isbn = {978-1-61284-927-0},
	author = {Kupka, Tomas and Griwodz, Carsten and Halvorsen, P{\r a}l},
	editor = {Anura Jayasumana {Pfeifer}}
}
@article {Simula.simula.708,
	title = {Search-Based Composition, Streaming and Playback of Video Archive Content},
	journal = {Multimedia Tools and Applications},
	number = {online first},
	year = {2011},
	abstract = {Locating content in existing video archives is both a time and bandwidth consuming process since users might have to download and manually watch large portions of superfluous videos. In this paper, we present two novel prototypes using an Internet based video composition and streaming system with a keyword-based search interface that collects, converts, analyses, indexes, and ranks video content. At user requests, the system can automatically sequence out portions of single videos or aggregate content from multiple videos to produce a single, personalized video stream on-the-fly.},
	doi = {10.1007/ s11042-011-0847-5},
	author = {Johansen, Dag and Halvorsen, P{\r a}l and Johansen, H{\r a}vard and Riiser, Haakon and Gurrin, Cathal and Olstad, Bj{\o}rn and Griwodz, Carsten and Kvalnes, {\r A}ge and Hurley, Joseph and Kupka, Tomas}
}
@inproceedings {Simula.telco.2,
	title = {Quality-Adaptive Scheduling for Live Streaming Over Multiple Access Networks},
	journal = {The 20th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV 2010)},
	year = {2010},
	month = {June},
	pages = {21-26},
	publisher = {ACM},
	type = {Conference},
	abstract = {Video streaming ranks among the most popular services offered through the Internet today. At the same time, accessing the Internet over public WiFi and 3G networks has become part of our everyday lives. However, streaming video in wireless environments is often subject to frequent periods of rebuffering and characterized by low picture quality. In particular, achieving smooth and quality-adaptive streaming of live video poses a big challenge in mobile scenarios. Building on the observation that the subjective video experience on mobile devices decreases when quality changes are more frequent than every 1 to 2 seconds, we present a client-side scheduler that retrieves segments of several video encodings over heterogeneous network interfaces simultaneously. By extending the DAVVI streaming platform with support for multihoming, the proposed scheduler{\textquoteright}s performance is experimentally evaluated. The results show that our scheduler reduces the video interruptions and achieves a higher and more stable average quality over multiple, truly heterogeneous wireless interfaces.},
	isbn = {978-1-4503-0043-8},
	author = {Evensen, Kristian and Kupka, Tomas and Kaspar, Dominik and Halvorsen, P{\r a}l and Griwodz, Carsten},
	editor = {Dick C. A. {Bulterman}}
}
@inproceedings {Simula.mpg.34,
	title = {VESP: a Video-Enabled Enterprise Search Platform},
	journal = {Proceeding of the International Conference on Data and Knowledge Engineering (ICDKE)},
	year = {2010},
	publisher = {IEEE Computer Society},
	type = {Conference},
	abstract = {In this paper, we present how to provide a novel and potentially disruptive multimedia service by modifying a widely deployed commercial enterprise search engine. The idea is to transparently integrate rich multimedia data with traditional textual-oriented query results. This includes that the search engine automatically discovers and extracts relevant scenes from a large knowledge repository of existing videos and produces a new, customized video of events matching the user query. To evaluate our prototype, we have performed experiments using a data set from a knowledge repository in Microsoft consisting of PowerPoint presentations with corresponding videos. Our initial results demonstrate that such integration can be implemented efficiently, and that potential users prefer to have the opportunity to enrich the search results with corresponding video.},
	author = {Halvorsen, P{\r a}l and Johansen, Dag and Olstad, Bj{\o}rn and Kupka, Tomas and Tenn{\o}e, Sverre},
	editor = {Xiang, Yang and Samarati, Pierangela and Hu, Jiankun and Zhou, Wanlei and Sadeghi, Ahmad-Reza}
}
@inproceedings {Simula.mpg.26,
	title = {VESP: Enriching Enterprise Document Search Results With Aligned Video Summarization},
	journal = {Proceedings of the International Conference on Multimedia (ACM MM)},
	year = {2010},
	month = {October},
	pages = {1603-1604},
	publisher = {ACM},
	type = {Conference},
	abstract = {In this demo, we present a video-enabled enterprise search platform (vESP), an application prototype that enhances a widely deployed commercial enterprise search engine with video streaming. In large enterprises, there exists a lot of information in form of presentations with corresponding video. Using our enhancements, a user can select and combine slides from different presentations generating a new slide deck dynamically and the corresponding video clips are concatenated and presented vis-a-vis the slides on-the-fly. The prototype is evaluated using a data set from Microsoft, and our initial user surveys indicate that the opportunity to enrich the search results with corresponding video is embraced by potential users.},
	isbn = {978-1-60558-933-6},
	author = {Halvorsen, P{\r a}l and Johansen, Dag and Olstad, Bj{\o}rn and Kupka, Tomas and Tenn{\o}e, Sverre},
	editor = {Shih-Fu Chang Albe {del Bimbo}}
}
