Ball Detection with the Aim of Corner Event Detection in Soccer Video

Hosseinkhani, Jila; Soltanian‐Zadeh, Hamid; Kamarei, Mahmoud; Staadt, Oliver

doi:10.1109/ispaw.2011.41

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…In that way, lowerlevel features with a higher spatial resolution are combined with higher-level features with a bigger receptive field. In [23], a ball detection algorithm is presented. Ball candidates are first extracted using features based on the shape, color, and size.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

AMİRLİ

Alemdar

2022

Academic Platform Journal of Engineering and Smart Systems

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Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axis.

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Section: Related Workmentioning

confidence: 99%

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

AMİRLİ

Alemdar

2022

Academic Platform Journal of Engineering and Smart Systems

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“…However researchers proposed a lot of methods for event detection in broadcast soccer videos, but they usually consider the most important events including goal [2,3,14,15,16], attack [2,7,14,16] and corner [2,14,16,17]. There are only a few researches that detect other events such as offside [2,14], penalty [16], card [2] and counterattack [16] events.…”

Section: Introductionmentioning

confidence: 99%

Counterattack detection in broadcast soccer videos using camera motion estimation

Sigari

Soltanian‐Zadeh

Kiani

et al. 2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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This paper presents a new method for counterattack detection using estimated camera motion and evaluates some classification methods to detect this event. To this end, video is partitioned to shots and view type of each shot is recognized first. Then, relative pan of the camera during far-view and mediumview shots is estimated. After weighting of pan value of each frame according to the type of shots, the video is partitioned to motion segments. Then, motion segments are refined to achieve better results. Finally, the features extracted from consecutive motion segments are investigated for counterattack detection. We propose two methods for counterattack detection: (1) rule-based (heuristic rules) and (2) SVM-based. Experiments show that the SVM classifier with linear or RBF kernel results in the best results.Index Terms-Broadcast soccer video, counterattack detection, camera motion estimation, event detection, video analysis.

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“…A. Color: The color attribute has been investigated more than the other bottomup factors in previous work [27][28][29][30] and it was shown that the warm, and saturated colors within a scene are generally more attractive for the HVS. In terms of the saliency of the colors in a scene, the human brain operation is very similar to contrast detection which means the colors that are more different from their surrounding areas are most likely to become distinctive for our visual system [10].…”

Section: Empirical Backgroundmentioning

confidence: 99%

“…Therefore, we used its energy to construct the feature vectors for textures of the images. We applied a dimension of 1 × 49 to the feature vectors for each pixel resulting from seven different preferred spatial frequencies (wavelength) and seven different equidistant orientations of [0, 30,45,60,90,120,135] degrees. Then, we computed the contrast of Gabor energy features using the center-surround mechanism weighted by a fovea mask similar to the color map.…”

Section: Texture Saliency Mapmentioning

confidence: 99%

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

Hosseinkhani¹

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I have a deep debt of gratitude to everyone who supported me throughout my Ph.D. study. Firstly, I would like to express my sincere appreciation and thanks to my Ph.D. thesis supervisor, Professor Chris Joslin for his guidance, support, motivation, and knowledge. Thanks must also go to my committee members for their helpful suggestions and discussions. Besides, I am mostly grateful to my wonderful parents for their unconditional love, support, and faith in me. Words cannot express how grateful I am to my mother, father, and sister for their support and encouragements. This accomplishment would not have been achievable without their constant support and love. I also owe much to my caring and supportive friends in Canada and elsewhere who helped me stay strong and keep moving forward during these years. v Table of Contents Abstract iii Acknowledgments v Table of Contents vi List of Tables ix List of Figures x Acronyms xiv Dup-Diagonal of-45 degree upward Ddown+ Diagonal of +45 degree downward Ddown-Diagonal of-45 degree downward xv

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Ball Detection with the Aim of Corner Event Detection in Soccer Video

Cited by 9 publications

References 15 publications

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

Counterattack detection in broadcast soccer videos using camera motion estimation

Gradual Saliency Detection in Video Sequences Using Bottom-up Attributes

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