A semi-supervised system for players detection and tracking in multi-camera soccer videos

Martin, R.G.; Martínez, José María Álvarez

doi:10.1007/s11042-013-1659-6

Cited by 20 publications

(5 citation statements)

References 16 publications

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“…These considerations are found since 2000 where Lefèvre et al [14] created a method based on a fast snake obtaining a processing time of 2 seconds per frame, with a implementation on Matlab for a sequence of 100 images of 24 bits, with width and height of 384 and 288 pixels respectively. Other works show similar speed performance using Matlab as a development plataform, [15] measures 4 FPS of the motion detection stage for a particle filter tracker, [16] obtain 3 FPS for their visual object tracker, [17] 3.3 FPS in a semi supervised system and [18] for a based adaptive Kalman Filter tracker. Matlab is good for developing and prototyping algorithms but shows lower values of FPS on the implementations of the tracking algorithms, this is because it is a scripting language that uses a JIT compiler to translate a script to machine code.…”

Section: Related Workmentioning

confidence: 91%

Parallelization of a Multipartite Graph Matching Algorithm for Tracking Multiple Football Players

Villalta

Siles

2018

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)

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This work describes the parallel methodology for a football tracking algorithm based on multipartite graphs using MPI and OpenMP. The proposed algorithm use a consumerproducer scheme to overlap the computing time of the two main procedures of the tracking algorithm: segmentation and tracking; as well a send and receive communication pattern to propagate the blob identities. We show how an hybrid system of data and task parallelization improves the execution time for 4K videos, achieving a speedup equal to 19.24 and a processing speed of 21.71 FPS with 128 threads.

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Section: Related Workmentioning

confidence: 91%

Parallelization of a Multipartite Graph Matching Algorithm for Tracking Multiple Football Players

Villalta

Siles

2018

2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)

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“…The performance of Multi-object tracking (MOT) can be measured by Multiple Object Tracking Accuracy (MOTA) as shown in (8).…”

Section: ) Performance Of Detection and Classification Of The Objects...mentioning

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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“…Various solution for the detection of players in soccer fields have been proposed. These methods include background subtraction and spectator region extractions [24,30,20,19,8,5]. However, there may remain some noisy regions in the image (line segments and other discontinuities in the image).…”

Section: State Of the Art 111 Player Detection In Soccer Fieldmentioning

confidence: 99%

Estimating the Number of Soccer Players Using Simulation-Based Occlusion Handling

Huda

Jensen

Gade

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Estimating the number of soccer players is crucial information for occupancy analysis and other monitoring activities in sports analysis. It depends on player detection in the field that should be independent of the environment and light conditions. Thermal cameras are therefore a better option over normal RGB cameras. Detection of nonoccluded players is doable but precise estimation of number of the players in groups is hard to achieve. Here we propose a novel method for estimating number of the players in groups using computer graphics and virtual simulations. Occlusion conditions are first classified by using distinctive set of features trained by a bagged tree classifier. Estimation of the number of players is then performed by maximum likelihood of probability density based approach to further classify the occluded players. The results show that the implemented strategy is capable of providing precise results even during occlusion conditions.

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A semi-supervised system for players detection and tracking in multi-camera soccer videos

Cited by 20 publications

References 16 publications

Parallelization of a Multipartite Graph Matching Algorithm for Tracking Multiple Football Players

Parallelization of a Multipartite Graph Matching Algorithm for Tracking Multiple Football Players

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

Estimating the Number of Soccer Players Using Simulation-Based Occlusion Handling

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