Multi-camera Sports Players 3D Localization with Identification Reasoning

Yang, Yukun; Zhang, Ruiheng; Wu, Wanneng; Peng, Yu; Xu, Min

doi:10.1109/icpr48806.2021.9412935

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…This approach failed to detect players in severe occlusion conditions [16]. Yang et al [17], [18] developed Bayesian based multi-dimensional model to create probabilistic and identified occupancy map to detect the player and identify the position of the player.…”

Section: A Player and Ball Detectionmentioning

confidence: 99%

DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

et al. 2022

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In real-world sports video analysis, identity switching caused by inter-object interactions is still a major difficulty for multi-player tracking. Due to similar appearances of players on the same squad, existing methodologies make it difficult to correlate detections and retain identities. In this paper, a novel approach (DeepPlayer-Track) is proposed to track the players and referees, by representing the deep features to retain the tracking identity. To provide identity-coherent trajectories, a sophisticated multi-player tracker is being developed further, encompassing deep features of player and referee identification. The proposed methodology consists of two parts: (i) the You Only Look Once (YOLOv4) can detect and classify players, soccer balls, referees, and background; (ii) Applying a modified deep feature association with a simple online real-time (SORT) tracking model which connects nodes from frame to frame using cosine distance and deep appearance descriptor to correlate the coefficient of the player identity (ID) which improved tracking performance by distinct identities. The proposed model achieved a tracking accuracy of 96% and 60% on MOTA and GMOTA metrics respectively with a detection speed of 23 frames per second (FPS).

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Section: A Player and Ball Detectionmentioning

confidence: 99%

DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

et al. 2022

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“…Several methods tracks players by using re-ID features [24,34,47,51,54]. Lu et al [34] use DPM [13] to detect basketball players.…”

Section: Tracking With Re-identificationmentioning

confidence: 99%

“…Re-ID features are computed thanks to the team classification, jersey number recognition and a poseguided feature embedding. To track soccer players, Yang et al [51] iteratively reduced the location and identification errors generated by the previous approach by creating a bayesian model that is optimized to best fit input pixel level segmentation and identification. Hurault et al [24] use a single network with a Faster R-CNN backbone [40] to detect small soccer players and extract re-ID features.…”

Section: Tracking With Re-identificationmentioning

confidence: 99%

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Efficient tracking of team sport players with few game-specific annotations

Maglo

Orcesi

Pham

2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillance. They show very convincing results when tested on public datasets such as the MOT challenge. However, the performance of these methods are not as satisfactory when applied to player tracking. Indeed, in addition to moving very quickly and often being occluded, the players wear the same jersey, which makes the task of reidentification very complex. Some recent tracking methods have been developed more specifically for the team sport context. Due to the lack of public data, these methods use private datasets that make impossible a comparison with them. In this paper, we propose a new generic method to track team sport players during a full game thanks to few human annotations collected via a semi-interactive system. Non-ambiguous tracklets and their appearance features are automatically generated with a detection and a reidentification network both pre-trained on public datasets. Then an incremental learning mechanism trains a Transformer to classify identities using few game-specific human annotations. Finally, tracklets are linked by an association algorithm. We demonstrate the efficiency of our approach on a challenging rugby sevens dataset. To overcome the lack of public sports tracking dataset, we publicly release this dataset at https://kalisteo.cea.fr/ index.php/free-resources/. We also show that our method is able to track rugby sevens players during a full match, if they are observable at a minimal resolution, with the annotation of only 6 few seconds length tracklets per player.

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“…Despite substantial advancements in sports analysis methods, as shown in recent studies [8,14,19,27,30,45,53], the majority of current tracking methods do not tackle all these tasks together. Solving each task individually is also not optimal as it overlooks the common objectives shared by all three tasks for accurately representing the individual, which could potentially benefit from a unified approach.…”

Section: Introductionmentioning

confidence: 99%

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Mansourian,

Somers,

De Vleeschouwer

et al. 2023

Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports

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Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the nonlinear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method, outperforms all existing tracking methods on the challenging SoccerNet tracking dataset. CCS CONCEPTS• Computing methodologies → Tracking.

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Multi-camera Sports Players 3D Localization with Identification Reasoning

Cited by 10 publications

References 25 publications

DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

Efficient tracking of team sport players with few game-specific annotations

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

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