| Title |
Recognition of basketball referee signals from videos using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) / |
| Authors |
Žemgulys, Julius ; Raudonis, Vidas ; Maskeliūnas, Rytis ; Damaševičius, Robertas |
| DOI |
10.1016/j.procs.2018.04.095 |
| Full Text |
|
| Is Part of |
Procedia computer science: the 9th international conference on ambient systems, networks and technologies, ANT-2018 and the 8th international conference on sustainable energy information technology, SEIT 2018, 8-11 May, 2018, Porto, Portugal.. Amsterdam : Elsevier. 2018, vol. 130, p. 953-960.. ISSN 1877-0509 |
| Keywords [eng] |
gesture recognition ; histogram of oriented gradients (HOG) ; support vector machine (SVM) ; basketball referee signals ; one-shot learning |
| Abstract [eng] |
Hand gestures, either static or dynamic, for human computer interaction in real time systems is an area of active research and with many possible applications. However, vision-based hand gesture interfaces for real-time applications require fast and extremely robust hand detection, and gesture recognition. Attempting to recognize gestures performed by officials in typical sports video places tremendous computational requirements on the image segmentation techniques. Here we propose an image segmentation technique based on the Histogram of Oriented Gradients (HOG) features that allows recognizing the signals of the basketball referee from videos. We achieve an accuracy of 97.5% using Support Vector Machine (SVM) for classification. |
| Published |
Amsterdam : Elsevier |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2018 |
| CC license |
|
