@article {39351,
	title = {FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	year = {2022},
	month = {Jan-01-2022},
	pages = {1 - 14},
	publisher = { IEEE},
	abstract = {The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide hard attention to the learned feature maps at different convolutional layers. The network also allows rectifying the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at https://github.com/nikhilroxtomar/FANet.},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2022.3159394},
	url = {https://ieeexplore.ieee.org/document/9741842},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Riegler, Michael and H{\r a}vard D. {Johansen} and Johansen, Dag and Rittscher, Jens and Halvorsen, P{\r a}l and Ali, Sharib}
}
@inproceedings {42695,
	title = {Video Analytics in Elite Soccer: A Distributed Computing Perspective},
	journal = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
	year = {2022},
	month = {06/2022},
	pages = {221-225},
	publisher = {IEEE},
	address = {Trondheim, Norway},
	abstract = {Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA{\textquoteright}s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.\&nbsp;},
	keywords = {analytics, football, soccer, Video},
	doi = {10.1109/SAM53842.2022.9827827},
	url = {https://ieeexplore.ieee.org/document/9827827},
	author = {Jha, Debesh and Rauniyar, Ashish and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l and Bagci, Ulas}
}
@inproceedings {28095,
	title = {Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner},
	journal = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR{\textquoteright}21)},
	year = {2021},
	pages = {57-61},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Fish crime is considered a global and serious problem for a healthy and sustainable development of one of mankind{\textquoteright}s important sources of food. Technological surveillance and control solutions are emerging as remedies to combat criminal activities, but such solutions might also come with impractical and negative side-effects and challenges. In this paper, we present the concept and design of a surveillance system in lieu of current surveillance trends striking a delicate balance between privacy of legal actors while simultaneously capturing evidence-based footage, sensory data, and forensic proofs of illicit activities. Our proposed novel approach is to assist human operators in the 24/7 surveillance loop of remote professional fishing activities with a privacy-preserving Artificial Intelligence (AI) surveillance system operating in the same proximity as the activities being surveyed. The system will primarily be using video surveillance data, but also other sensor data captured on the fishing vessel. Additionally, the system correlates with other sources such as reports from other fish catches in the approximate area and time, etc. Only upon true positive flagging of specific potentially illicit activities by the locally executing AI algorithms, can forensic evidence be accessed from this physical edge, the fishing vessel. Besides a more privacy-preserving solution, our edge-based AI system also benefits from much less data that has to be transferred over unreliable, low-bandwidth satellite-based networks.},
	isbn = {9781450385299},
	doi = {10.1145/346394410.1145/3463944.3469102},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3463944},
	author = {Tor-Arne Schmidt {Nordmo} and Aril B. {Ovesen} and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag},
	editor = {Dao, Minh-Son and Dang-Nguyen, Duc-Tien and Riegler, Michael}
}
@inproceedings {28229,
	title = {Njord: An out-in-the-wild real world fish vessel catch analysis dataset},
	journal = {Arctic Frontiers},
	year = {2021},
	publisher = {Arctic Frontiers},
	address = {Troms{\o}, Norway},
	author = {Riegler, Michael and Johansen, Dag and Bj{\o}rn Aslak {Juliussen} and Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and Jon Petter {Rui}}
}
@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 {26246,
	title = {Quantified Soccer Using Positional Data: A Case Study},
	journal = {Frontiers in Physiology},
	volume = {9},
	year = {2018},
	month = {Jun-07-2018},
	pages = {866},
	publisher = {Frontiers},
	abstract = {Performance development in international soccer is undergoing a silent revolution fueled by the rapidly increasing availability of athlete quantification data and advanced analytics. Objective performance data from teams and individual players are increasingly being collected automatically during practices and more recently also in matches after FIFA{\textquoteright}s 2015 approval of wearables in electronic performance and tracking systems. Some clubs have even started collecting data from players outside of the sport arenas. Further algorithmic analysis of these data might provide vital insights for individual training personalization and injury prevention, and also provide a foundation for evidence-based decisions for team performance improvements. This paper presents our experiences from using a detailed radio-based wearable positioning data system in an elite soccer club. We demonstrate how such a system can detect and find anomalies, trends, and insights vital for individual athletic and soccer team performance development. As an example, during a normal microcycle (6 days) full backs only covered 26\% of the sprint distance they covered in the next match. This indicates that practitioners must carefully consider to proximity size and physical work pattern in microcycles to better resemble match performance. We also compare and discuss the accuracy between radio waves and GPS in sampling tracking data. Finally, we present how we are extending the radio-based positional system with a novel soccer analytics annotation system, and a real-time video processing system using a video camera array. This provides a novel toolkit for modern forward-looking soccer coaches that we hope to integrate in future studies.\&nbsp;},
	doi = {10.3389/fphys.2018.00866},
	url = {https://www.frontiersin.org/article/10.3389/fphys.2018.00866/fullhttps://www.frontiersin.org/article/10.3389/fphys.2018.00866/full},
	author = {Svein A. {Pettersen} and H{\r a}vard D. {Johansen} and Ivan A. M. {Baptista} and Halvorsen, P{\r a}l and Johansen, Dag}
}
