AFL Player Detection and Tracking

Faulkner, Hayden; Dick, Anthony

doi:10.1109/dicta.2015.7371226

Cited by 6 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The task poses additional challenges to generic object detection, including camera distortion and fast movement (for broadcast videos), as well as frequent crowded scenes. Thaler et al [17] and Faulkner et al [7] adopt HOG and Haar features to detect and classify players in a sliding window for soccer and football. Acuna [1] introduces an end-to-end CNN-based object detector in the basketball.…”

Section: Related Work 21 Object Detection In Sportsmentioning

confidence: 99%

Detecting and Matching Related Objects with One Proposal Multiple Predictions

Liu¹,

Hafemann²,

Jamieson³

et al. 2021

Preprint

View full text Add to dashboard Cite

Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for some sports tracking players is sufficient for game analysis, sports like hockey, tennis and polo may require additional detections, that include the object the player is holding (e.g. racket, stick). The baseline solution for this problem involves detecting these objects as separate classes, and matching them to player detections based on the intersection over union (IoU). This approach, however, leads to poor matching performance in crowded situations, as it does not model the relationship between players and objects. In this paper, we propose a simple yet efficient way to detect and match players and related objects at once without extra cost, by considering an implicit association for prediction of multiple objects through the same proposal box. We evaluate the method on a dataset of broadcast ice hockey videos, and also a new public dataset we introduce called COCO +Torso. On the ice hockey dataset, the proposed method boosts matching performance from 57.1% to 81.4%, while also improving the meanAP of player+stick detections from 68.4% to 88.3%. On the COCO +Torso dataset, we see matching improving from 47.9% to 65.2%. The COCO +Torso dataset, code and pre-trained models will be released at https: //github.com/foreverYoungGitHub/detectand-match-related-objects.

show abstract

Section: Related Work 21 Object Detection In Sportsmentioning

confidence: 99%

Detecting and Matching Related Objects with One Proposal Multiple Predictions

Liu¹,

Hafemann²,

Jamieson³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, noise should be expected due to, e.g., other moving objects, similar colors in foreground and background, changing lighting conditions, and shadows. It has also been proposed to use classic person detection methods like using the AdaBoost algorithm for training a linear classifier with HOG features for detecting players in Australian Rules Football [11], or similarly with AdaBoost and Haar features for player detection in basketball [21] and baseball [26].…”

Section: Related Workmentioning

confidence: 99%

Multimodal and multiview distillation for real-time player detection on a football field

Cioppa

Deliège

Huda

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

Monitoring the occupancy of public sports facilities is essential to assess their use and to motivate their construction in new places. In the case of a football field, the area to cover is large, thus several regular cameras should be used, which makes the setup expensive and complex. As an alternative, we developed a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera. In this work, we train a network in a knowledge distillation approach in which the student and the teacher have different modalities and a different view of the same scene. In particular, we design a custom data augmentation combined with a motion detection algorithm to handle the training in the region of the fisheye camera not covered by the thermal one. We show that our solution is effective in detecting players on the whole field filmed by the fisheye camera. We evaluate it quantitatively and qualitatively in the case of an online distillation, where the student detects players in real time while being continuously adapted to the latest video conditions.

show abstract

“…These limitations severely impact the aforementioned tracking methods' performance, serving to inhibit the application of athlete detection and tracking methods in AF [4]. One study used a custom person detector and team classifier for detection and then tracked the athletes across frames of broadcast video with a combination of Kalman filters and energy minimisation techniques [42]. The results of this investigation struggled to overcome the changes in lighting conditions and frequent occlusions of athletes.…”

Section: Introductionmentioning

confidence: 99%

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

Born,

Mundt,

Mian

et al. 2024

View full text Add to dashboard Cite

The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools.

show abstract

AFL Player Detection and Tracking

Cited by 6 publications

References 25 publications

Detecting and Matching Related Objects with One Proposal Multiple Predictions

Detecting and Matching Related Objects with One Proposal Multiple Predictions

Multimodal and multiview distillation for real-time player detection on a football field

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

Contact Info

Product

Resources

About