ForzaSys (Norway)

Precise mapping of ice hockey rinks is critical for applications such as player tracking, game strategy analysis, and broadcast enhancements. Traditional methods often rely on manual annotations or simplistic models that fail to account for the rink's complex geometry and dynamic in-game conditions. To address these limitations, we present HockeyRink, a novel dataset comprising 56 meticulously annotated keypoints corresponding to significant landmarks on a standard hockey rink, including face-off dots, goalposts, and blue lines. Leveraging the YOLOv8-Large pose estimation architecture, we adapted the model to treat the rink as a single 'pose' object, enabling accurate keypoint predictions tailored to the nuances of hockey rink imagery. Our dataset, derived from diverse hockey game footage from the Swedish Hockey League (SHL), facilitates applications in homography estimation, 2D/3D scene mapping, and tactical overlays. By detecting and mapping rink keypoints, one can compute precise transformations between the image plane and the rink's physical dimensions, enabling the overlay of player trajectories and tactical insights onto broadcast footage. This work addresses the unique challenges of ice hockey, such as occlusions, rapid camera movements, and varying lighting conditions, and offers a robust foundation for future research and innovation in sports analytics. The HockeyRink dataset and trained model are openly available at: https://huggingface.co/SimulaMet-HOST/HockeyRink.

In the rapidly evolving field of sports analytics, the automation of targeted video processing is a pivotal advancement. We propose PlayerTV, an innovative framework which harnesses state-of-the-art AI technologies for automatic player tracking and identification in soccer videos. By integrating object detection and tracking, Optical Character Recognition (OCR), and color analysis, PlayerTV facilitates the generation of player-specific highlight clips from extensive game footage, significantly reducing the manual labor traditionally associated with such tasks. Preliminary results from the evaluation of our core pipeline, tested on a dataset from the Norwegian Eliteserien league, indicate that PlayerTV can accurately and efficiently identify teams and players, and our interactive Graphical User Interface (GUI) serves as a user-friendly application wrapping this functionality for streamlined use.

The rapid advancement of technology has been revolutionizing the field of sports media, where there is a growing need for sophisticated data processing methods. Current methodologies for extracting information from soccer broadcast videos to generate game highlights and summaries for social media are predominantly manual and rely heavily on text-based NLP techniques, overlooking the rich visual and auditory information available. In response to this challenge, our research introduces SoccerSum, a tool that innovates in the field by integrating computer vision, audio analysis with advanced language models like GPT-4. This multimodal approach enables automated, enriched content summarization, including detection of players and key field elements, thereby enhancing the metadata used in summarization algorithms. SoccerSum uniquely combines textual and visual data, offering a comprehensive solution for generating accurate, platform-specific content. This development represents a significant advancement in automated, data-driven sports media dissemination, and sets a new benchmark in the realm of soccer information extraction. A video of the demo can be found here: https://youtu.be/za4VIi2ARXY.

Sports multimedia is among the most prominent types of content distributed across social media today, and the retargeting of videos for diverse aspect ratios is essential for a suitable representation on different social media platforms. In this respect, ice hockey is quite challenging due to its agile movement pattern and speed, and because the main reference point (puck) is very small. In this paper, we introduce a novel pipeline for intelligent video cropping tailored for ice hockey. Our main goal is to identify regions of interest in video frames by detecting and tracking the hockey puck using state-of-the-art AI models. Our pipeline employs scene detection, object detection, outlier detection, and smoothing as key features. Our proposed pipeline called SmartCrop-H is not only highly efficient and configurable with respect to target aspect ratios, but also addresses the automation needs in this domain. Our comprehensive evaluation, comprising objective and subjective measures, shows the overall efficiency of the entire pipeline, including assessments of both the individual components and the end-to-end system performance. We also demonstrate the practical applicability of SmartCrop-H with a user study, which indicates that our framework performs on par with, or even surpasses, professional tools in terms of output quality. INDEX TERMS AI, aspect ratio, cropping, ice hockey, social media, video processing.

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.