EVENTS RECOGNITION FOR A SEMI-AUTOMATIC ANNOTATION OF SOCCER VIDEOS: A STUDY BASED DEEP LEARNING

Kazi Tani, L. F.; Ghomari, A.; Kazi Tani, M. Y.

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.

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Kazi Tani, L. F. / Ghomari, A. / Kazi Tani, M. Y.: EVENTS RECOGNITION FOR A SEMI-AUTOMATIC ANNOTATION OF SOCCER VIDEOS: A STUDY BASED DEEP LEARNING. 2019. Copernicus Publications.

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Rechteinhaber: L. F. Kazi Tani et al.

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