A Semi-Automatic Soccer Video Annotation System based on Ontology Paradigm

Tani, Lamia Fatiha Kazi; Ghomari, Abdelghani; Tani, Mohammed Yassine Kazi

doi:10.1109/iacs.2019.8809161

Cited by 2 publications

(1 citation statement)

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“…We also see from the measurement that ResNet gives the best result as a backbone of Mask R-CNN. Future works will include collecting more videos and images finding more events of interest to improve the effectiveness of the network .We propose to follow the study (Kazi Tani et al, 2019) as a complete solution to this one. We perform ontology to infer the proper event.…”

Section: Resultsmentioning

confidence: 99%

Events Recognition for a Semi-Automatic Annotation of Soccer Videos: A Study Based Deep Learning

Tani

Ghomari

Tani

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> 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.</p>

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Section: Resultsmentioning

confidence: 99%