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Multi Player Tracking in Ice Hockey with Homographic Projections

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abstract

Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities. Tracking players from monocular broadcast feeds is an important computer vision problem offering various downstream analytics and enhanced viewership experience. However, existing trackers encounter significant difficulties in dealing with occlusions, blurs, and agile player movements prevalent in telecast feeds. In this work, we propose a novel tracking approach by formulating MOT as a bipartite graph matching problem infused with homography. We disentangle the positional representations of occluded and overlapping players in broadcast view, by mapping their foot keypoints to an overhead rink template, and encode these projected positions into the graph network. This ensures reliable spatial context for consistent player tracking and unfragmented tracklet prediction. Our results show considerable improvements in both the IDsw and IDF1 metrics on the two available broadcast ice hockey datasets.

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2025 1

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representative citing papers

Ice Hockey Puck Localization Using Contextual Cues

cs.CV · 2025-06-04 · conditional · novelty 5.0

PLUCC fuses player-segmentation context with multiscale image features to localize the puck, reporting 12.2% higher average precision than the tested baselines and a new rink-space error metric.

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  • Ice Hockey Puck Localization Using Contextual Cues cs.CV · 2025-06-04 · conditional · none · ref 31 · internal anchor

    PLUCC fuses player-segmentation context with multiscale image features to localize the puck, reporting 12.2% higher average precision than the tested baselines and a new rink-space error metric.