EVENTS RECOGNITION FOR A SEMI-AUTOMATIC ANNOTATION OF SOCCER VIDEOS: A STUDY BASED DEEP LEARNING
| Autor: | M. Y. Kazi Tani, Abdelghani Ghomari, L. F. Kazi Tani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
lcsh:Applied optics. Photonics
Artificial neural network business.industry Computer science lcsh:T Deep learning lcsh:TA1501-1820 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Convolutional neural network lcsh:Technology Annotation lcsh:TA1-2040 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Semi automatic business lcsh:Engineering (General). Civil engineering (General) computer |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W16, Pp 135-141 (2019) |
| ISSN: | 2194-9034 1682-1750 |
| Popis: | In this work, we propose an efficient way of web video annotation in soccer domain. To achieve this, it is necessary to enjoy different architectures of deep learning. We aim at realising a system of annotation able to recognise several events from information of the object that is the ball in our case, in order to fuse them as a part of actions in video. We propose to use Deep Neural Network (DNN) to detect ball and actions. However, Mask R-CNN can play a very important role for features extracted as an output using a training network on ImageNet dataset. The Mask R-CNN is chosen as a method using different CNN as backbone (convolutional Neural Network) ResNet50, ResNet101 and ResNet152, VGG16, VGG 19. We experimentally verify the effectiveness of the proposed method in the test phase. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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