@article {28314,
	title = {Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities},
	journal = {Machine Learning and Knowledge Extraction},
	volume = {3},
	year = {2021},
	month = {12/2021},
	pages = {1030 - 1054},
	publisher = {MDPI},
	abstract = {Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.},
	doi = {10.3390/make3040051},
	url = {https://www.mdpi.com/2504-4990/3/4/51/pdf},
	author = {Olav Andre Nerg {Rongved} and Stige, Markus and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Zouganeli, Evi and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {28007,
	title = {Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events},
	journal = {International Journal of Semantic Computing},
	volume = {15},
	number = {2},
	year = {2021},
	month = {Jan-06-2021},
	pages = {161 - 187},
	publisher = {World Scientific},
	abstract = {Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.},
	keywords = {3d CNN, classification, Detection, soccer events, spotting},
	issn = {1793-351X},
	doi = {10.1142/S1793351X2140002X},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S1793351X2140002X},
	author = {Olav A. Nerg{\r a}r {Rongved} and Hicks, Steven and Thambawita, Vajira and H{\r a}kon Kvale {Stensland} and Zouganeli, Evi and Johansen, Dag and Midoglu, Cise and Riegler, Michael and Halvorsen, P{\r a}l}
}
