What Players do with the Ball: A Physically Constrained Interaction Modeling

Maksai, Andrii; Wang, Xinchao; Fua, Pascal

doi:10.1109/cvpr.2016.111

Cited by 74 publications

(42 citation statements)

References 28 publications

(68 reference statements)

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“…Compared to the conventional feature extraction method, the improved ball detector is more suitable for the small ball detection. RANSAC [47] 0.50 MIP-with-constraints [12] 0.62 Ball-position-measurement-system [14] 0. • iECO versus iKCF: When two correlation filter related tracking methods are adopted for 2D ball tracking, the problem of tracking template drift would be easily occurred due to occlusions.…”

Section: Discussionmentioning

confidence: 99%

Multi‐camera 3D ball tracking framework for sports video

Wu¹,

Liang³

et al. 2020

IET Image Processing

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Accurate ball tracking in sports is vital for automatic sports analysis yet it is challenging mainly due to the small size and occlusions. This study proposes a novel multi-camera 3D ball tracking (MBT) framework for sports video. The proposed framework consists of four parts: 2D ball detection, 2D ball tracking, 3D position fusion, and 3D ball tracking. In 2D aspect, the multi-scale features are introduced to enhance the 2D ball detection, and the 2D ball tracking is also improved by exploring cross-view information to handle the occlusion and timely updating tracking model with detection results to alleviate the problem of tracking drift. For 3D ball, a novel 3D position fusion method is proposed to optimise the ball position and the 3D ball tracking approach with improved Kalman filter is finally applied to ensure a smooth 3D ball trajectory. Moreover, compared to the existing products in commercial, the proposed framework does not require any special equipment and is thus low cost. Extensive experiments for 2D and 3D ball on a public dataset demonstrate that the proposed framework is robust to ball tracking in sports video, even in the presence of environmental interferences, substantial occlusions, and even calibration errors.

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Section: Discussionmentioning

confidence: 99%

Multi‐camera 3D ball tracking framework for sports video

Wu¹,

Liang³

et al. 2020

IET Image Processing

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“…Tracking and data association is a key issue where the role of the players provides a strong cue to disambiguate appearance based tracking [22]. Events such as ball movement, can be also recognized using a spatiotemporal analysis [24]. As players behave strategi-cally and collectively, their group movement can be predicted [17] and the ball can be localized without detection.…”

Section: Related Workmentioning

confidence: 99%

Am I a Baller? Basketball Performance Assessment from First-Person Videos

Bertasius

Park

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

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This paper presents a method to assess a basketball player's performance from his/her first-person video. A key challenge lies in the fact that the evaluation metric is highly subjective and specific to a particular evaluator. We leverage the first-person camera to address this challenge. The spatiotemporal visual semantics provided by a first-person view allows us to reason about the camera wearer's actions while he/she is participating in an unscripted basketball game. Our method takes a player's first-person video and provides a player's performance measure that is specific to an evaluator's preference.To achieve this goal, we first use a convolutional LSTM network to detect atomic basketball events from first-person videos. Our network's ability to zoom-in to the salient regions addresses the issue of a severe camera wearer's head movement in first-person videos. The detected atomic events are then passed through the Gaussian mixtures to construct a highly non-linear visual spatiotemporal basketball assessment feature. Finally, we use this feature to learn a basketball assessment model from pairs of labeled first-person basketball videos, for which a basketball expert indicates, which of the two players is better.We demonstrate that despite not knowing the basketball evaluator's criterion, our model learns to accurately assess the players in real-world games. Furthermore, our model can also discover basketball events that contribute positively and negatively to a player's performance.

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“…Approaches in this class have proved successful at both tracking people [6], [34], [7], [9], [10], [35], [36], [37], tracking cells [12], [13], [1], and tracking more generic particles including molecules and viruses [38].…”

Section: Tracking By Detectionmentioning

confidence: 99%

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Türetken

Wang

Becker

et al. 2017

IEEE Trans. Med. Imaging

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Abstract-We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to partial occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.

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What Players do with the Ball: A Physically Constrained Interaction Modeling

Cited by 74 publications

References 28 publications

Multi‐camera 3D ball tracking framework for sports video

Multi‐camera 3D ball tracking framework for sports video

Am I a Baller? Basketball Performance Assessment from First-Person Videos

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

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