@inproceedings {26629,
	title = {Real-time Analysis of Physical Performance Parameters in Elite Soccer},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Technology is having vast impact on the sports in- dustry, and in particular soccer. All over the world, soccer teams are adapting digital information systems to quantify performance metrics. The goal is to assess strengths and weaknesses of indi- vidual players, training regimes, and play strategies; to improve performance and win games. However, most existing methods rely on post-game analytic. This limits coaches to review games in retrospect without any means to do changes during sessions. In collaboration with an elite soccer club, we have developed Metrix which is a computerized toolkit for coaches to perform real- time monitoring and analysis of the players{\textquoteright} performance. Using sensor technology to track movement, performance parameters are instantly available to coaches through a mobile phone client. Metrix provides coaches with a toolkit to individualize training load to different playing positions on the field, or to the player himself. Our results show that Metrix is able to quantify player performance and propagate it to coaches in real-time during a match or practice, i.e., latency is below 100 ms on the field. In our initial user evaluation, the coaches express that this is a valuable asset in day-to-day work.},
	doi = {10.1109/CBMI.2019.8877422},
	author = {Andreassen, Kim and Johansen, Dag and Johansen, H{\r a}vard and Baptista, Ivan and Svein Arne {Pettersen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@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 {24641,
	title = {Multimedia and Medicine: Teammates for Better Disease Detection and Survival},
	journal = {ACM Multimedia},
	year = {2016},
	month = {10/2016},
	publisher = {ACM},
	address = {Amsterdam, The Netherlands, The Netherlands},
	doi = {http://dx.doi.org/10.1145/2964284.2976760},
	author = {Riegler, Michael and Griwodz, Carsten and Spampinato, Concetto and de Lange, Thomas and Sigrun L. {Eskeland} and Pogorelov, Konstantin and Tavanapong, Wallapak and Peter Thelin {Schmidt} and Gurrin, Cathal and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l}
}
@inproceedings {Pettersen2014a,
	title = {Soccer Video and Player Position Dataset},
	journal = {Proceedings of the 5th ACM Multimedia Systems Conference on - MMSys {\textquoteright}14},
	year = {2014},
	pages = {18{\textendash}23},
	publisher = {ACM Press},
	address = {New York, New York, USA},
	abstract = {Abstract This paper presents a dataset of body-sensor traces and corresponding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Troms{\o}, Norway. Player data, including field position , heading, and speed are sampled at 20Hz ... $\backslash$n},
	isbn = {9781450327053},
	doi = {10.1145/2557642.2563677},
	url = {http://dl.acm.org/citation.cfm?doid=2557642.2563677},
	author = {Svein Arne {Pettersen} and Halvorsen, P{\r a}l and Johansen, H{\r a}vard and Berg-Johansen, Vegard and Vamsidhar Reddy {Gaddam} and Mortensen, Asgeir and Langseth, Ragnar and Griwodz, Carsten and H{\r a}kon Kvale {Stensland}}
}
@inproceedings {mmsys.2014-02-18.0527842670,
	title = {Soccer Video and Player Position Dataset},
	journal = {Proceedings of the 5th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2014},
	publisher = {ACM},
	type = {Conference},
	abstract = {This paper presents a dataset of body-sensor traces and corresponding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Troms{\o}, Norway.Player data, including field position, heading, and speed are sampled at 20 Hz using the highly accurate ZXY Sport Tracking system. Additional per-player statistics, like total distance covered and distance covered in different speed classes, are also included with a 1 Hz sampling rate. The provided videos are in high-definition and captured using two stationary camera arrays positioned at an elevated position above the tribune area close to the center of the field. The camera array is configured to cover the entire soccer field, and each camera can be used individually or as a stitched panorama video. This combination of body-sensor data and videos enables computer-vision algorithms for feature extraction, object tracking, background subtraction, and similar, to be tested against the ground truth contained in the sensor traces.},
	author = {Svein Arne {Pettersen} and Johansen, Dag and Johansen, H{\r a}vard and Berg-Johansen, Vegard and Vamsidhar Reddy {Gaddam} and Mortensen, Asgeir and Langseth, Ragnar and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l}
}
@inproceedings {Simula.simula.2150,
	title = {Combining Video and Player Telemetry for Evidence-Based Decisions in Soccer},
	journal = {Proceedings of the International Congress on Sports Science Research and Technology Support (icSPORTS)},
	year = {2013},
	month = {September},
	type = {Conference},
	abstract = {Technology is changing how soccer clubs train and interact with their supporters. Systems that provide acquisition and visualization of low-level player telemetry, like distance covered and speed, are already being widely adopted. A key observation is that such data when correlated with actual in-game video footage is a powerful tool for evidence-based decisions. As data volume and complexity grow, efficient tools for auto- mated high-precision retrieval become essential. This paper describes the unique combination of a radio-based sensor platform and several custom video retrieval systems in operational use at Troms{\o} Idrettslag (TIL), a Norwegian premier league soccer club. The systems have been developed using an experimental computer- science method where several prototypes were built and deployed for evaluation in close collaboration with the intended users. Although our method of computer-system prototyping has not yielded commercial quality products, it has enabled us to construct several novel systems combining low-level player telemetry with video retrieval, annotations, and user-centric security.},
	author = {Johansen, H{\r a}vard and Svein Arne {Pettersen} and Halvorsen, P{\r a}l and Johansen, Dag},
	editor = {Jo{\~a}o Barreiros {Correia}}
}
@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.mpg.22,
	title = {Composing Personalized Video Playouts Using Search},
	journal = {Proceedings of the International Workshop on Visual Content Identification and Search (VCIDS) - ICME workshops},
	year = {2010},
	month = {July},
	pages = {1534-1539},
	publisher = {IEEE},
	type = {Workshop},
	abstract = {We conjecture that composition of video events from various sources into personalized video playouts will become an important part of next generation streaming systems. Here, video search is a key component since it enables users to retrieve candidate video events based on their interests. One of the main challenges, however, is to analyze the videos in order to correctly identify the various events used to annotate and index the video data. Key problems with current video analysis solutions include that they 1) are complex and therefore require a lot of processing time resulting in large delays; 2) that they can only identify a limited set of events; and 3) that they are still too inaccurate, both giving false positives and failing to find all events. In our DAVVI prototype, we therefore extract metadata for our video search engine by combining existing automatic video analysis tools with currently untapped textual information available in the Internet. This provides an end-user experience where textual query results can be combined dynamically into seamless, highly personalized video playouts using an adaptive torrent-like HTTP streaming solution.},
	isbn = {978-1-4244-7491-2},
	author = {Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l and Olstad, Bj{\o}rn and Gurrin, Cathal and Griwodz, Carsten},
	editor = {Lu, Jian and Hua, Xian-Sheng and China Dong {Xu}}
}
@inproceedings {Simula.ND.391,
	title = {DAVVI: a Prototype for the Next Generation Multimedia Entertainment Platform},
	journal = {ACM Multimedia Conference},
	year = {2009},
	note = {Demo paper.},
	month = {October},
	pages = {989-990},
	publisher = {ACM},
	type = {Conference},
	abstract = {In this demo, we present DAVVI, a prototype of the next generation multimedia entertainment platform. It delivers multi-quality video content in a torrent-similar way like known systems from Move Networks, Microsoft and Apple do. However, it also provides a brand new, personalized user experience. Through applied search, personalization and recommendation technologies, end-users can efficiently search and retrieve highlights and combine arbitrary events in a customized manner using drag and drop. The created playlists of video segments are then delivered back to the system to improve future search and recommendation results. Here, we demonstrate this system using \mbox{a soccer example.}},
	isbn = {978-1-60558-608-3},
	author = {Johansen, Dag and Johansen, H{\r a}vard and Aarflot, Tjalve and Hurley, Joseph and Kvalnes, {\r A}ge and Gurrin, Cathal and Sav, Sorin and Olstad, Bj{\o}rn and Aaberg, Erik and Endestad, Tore and Riiser, Haakon and Griwodz, Carsten and Halvorsen, P{\r a}l},
	editor = {Gao, Wen and Rui, Yong and Hanjalic, Alan and Xu, Changsheng and Steinbach, Eckehard and Abdulmotaleb El {Saddik} and Zhou, Michelle}
}
