Ice-hockey puck detection and tracking for video highlighting

Yakut, Mehmet; Kehtarnavaz, Nasser

doi:10.1007/s11760-015-0764-6

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Wang et al [22] propose a unique conditional random field (CRF) based algorithm to exploit the contextual relationship between the players and ball for ball tracking. Yakut et al [23] used background subtraction to track hockey puck in zoomed in broadcast videos for short time intervals. The puck tracking performance deteriorated with high motion blur, fast camera motion and occlusions.…”

Section: Ball Tracking Using Traditional Computer Visionmentioning

confidence: 99%

Puck localization and multi-task event recognition in broadcast hockey videos

Vats¹,

Fani²,

Clausi³

et al. 2021

Preprint

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Puck localization is an important problem in ice hockey video analytics useful for analyzing the game, determining play location, and assessing puck possession. The problem is challenging due to the small size of the puck, excessive motion blur due to high puck velocity and occlusions due to players and boards. In this paper, we introduce and implement a network for puck localization in broadcast hockey video. The network leverages expert NHL play-by-play annotations and uses temporal context to locate the puck. Player locations are incorporated into the network through an attention mechanism by encoding player positions with a Gaussian-based spatial heatmap drawn at player positions. Since event occurrence on the rink and puck location are related, we also perform event recognition by augmenting the puck localization network with an event recognition head and training the network through multi-task learning. Experimental results demonstrate that the network is able to localize the puck with an AUC of 73.1% on the test set. The puck location can be inferred in 720p broadcast videos at 5 frames per second. It is also demonstrated that multitask learning with puck location improves event recognition accuracy.

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Section: Ball Tracking Using Traditional Computer Visionmentioning

confidence: 99%

Puck localization and multi-task event recognition in broadcast hockey videos

Vats¹,

Fani²,

Clausi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The discriminative methods learn a binary classifier, which is then used to classify a candidate as the target or background [5,8,14,16,[30][31][32][33][34]. In [30], Yakut and Kehtarnavaz proposed to track ice-hockey pucks by combining three pieces of information in ice-hockey video frames using an adaptive gray-level thresholding method.…”

Section: Related Workmentioning

confidence: 99%

“…In [30], Yakut and Kehtarnavaz proposed to track ice-hockey pucks by combining three pieces of information in ice-hockey video frames using an adaptive gray-level thresholding method. In [31], Topkaya et al proposed a multiple object tracking method using tracklet clustering, which first obtains short yet reliable tracklets and then clusters the tracklets over time based on color and spatial and temporal attributes.…”

Section: Related Workmentioning

confidence: 99%

Visual Tracking Based on Discriminative Compressed Features

Liu

Wang

2018

Advances in Multimedia

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Visual tracking is a challenging research topic in the field of computer vision with many potential applications. A large number of tracking methods have been proposed and achieved designed tracking performance. However, the current state-of-the-art tracking methods still can not meet the requirements of real-world applications. One of the main challenges is to design a good appearance model to describe the target's appearance. In this paper, we propose a novel visual tracking method, which uses compressed features to model target's appearances and then uses SVM to distinguish the target from its background. The compressed features were obtained by the zero-tree coding on multiscale wavelet coefficients extracted from an image, which have both the low dimensionality and discriminate ability and therefore ensure to achieve better tracking results. The experimental comparisons with several stateof-the-art methods demonstrate the superiority of the proposed method.

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Automatic Playing Field Detection and Dominant Color Extraction in Sports Video Shots of Different View Types

Choroś

2016

Advances in Intelligent Systems and Computing

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Ice-hockey puck detection and tracking for video highlighting

Cited by 7 publications

References 11 publications

Puck localization and multi-task event recognition in broadcast hockey videos

Puck localization and multi-task event recognition in broadcast hockey videos

Visual Tracking Based on Discriminative Compressed Features

Automatic Playing Field Detection and Dominant Color Extraction in Sports Video Shots of Different View Types

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