An Efficient Ball and Player Detection in Broadcast Tennis Video

2015 ◽  
pp. 427-436 ◽  
Author(s):  
M. Archana ◽  
M. Kalaiselvi Geetha
Keyword(s):  
Player Detection

scholarly journals Automatic player detection, labeling and tracking in broadcast soccer video

2009 ◽  
Vol 30 (2) ◽  
pp. 103-113 ◽  
Author(s):  
Jia Liu ◽  
Xiaofeng Tong ◽  
Wenlong Li ◽  
Tao Wang ◽  
Yimin Zhang ◽  
...  
Keyword(s):  
Soccer Video ◽  
Player Detection

Player detection using one-class SVM

2012 ◽  
Author(s):  
Xuefeng Bai ◽  
Tiejun Zhang ◽  
Chuanjun Wang ◽  
Qiong Li ◽  
Xiamu Niu
Keyword(s):  
Player Detection

scholarly journals An Automated Player Detection and Tracking in Basketball Game

2019 ◽  
Vol 58 (3) ◽  
pp. 625-639 ◽  
Author(s):  
P. K. Santhosh and B. Kaarthick
Keyword(s):  
Detection And Tracking ◽  
Basketball Game ◽  
Player Detection

scholarly journals Badminton player detection using faster region convolutional neural network

2019 ◽  
Vol 14 (3) ◽  
pp. 1330 ◽  
Author(s):  
Nur Azmina Rahmad ◽  
Nur Anis Jasmin Sufri ◽  
Nurul Hamizah Muzamil ◽  
Muhammad Amir As'ari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sport Performance ◽  
Automated System ◽  
Average Precision ◽  
Detector Performance ◽  
Sport Video ◽  
Broadcast Video ◽  
Player Detection ◽  
Detector Model

Nowadays, coaches and sport analyst are concerning about sport performance analysis through sport video match. However, they still used conventional method which is through manual observation of the full video that is very troublesome because they might miss some meaningful information presence in the video. Several previous studies have discussed about tracking ball movements, identification of player based on jersey color and number as well as player movement detection in various type of sport such as soccer and volleyball but not in badminton. Therefore, this study focused on developing an automated system using Faster Region Convolutional Neural Network (Faster R-CNN) to track the position of the badminton player from the sport broadcast video. In preparing the dataset for training and testing, several broadcast videos were converted into image frames before labelling the region which indicate the players. After that, several different trained Faster R-CNN detectors were produced from the dataset before tested with different set of videos to evaluate the detector performance. In evaluating the performance of each detector model, the average precision was obtained from precision recall graph. As a result, this study revealed that the detector successfully detects the player when the detector is being fed with more generalized dataset.

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