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

Abstract

<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>

Deep Learning-Based Enhanced ISAR-RID Imaging Method

“…In recent years, deep learning has garnered significant attention and shown successful outcomes in a wide range of fields, including speech recognition [30], automatic video annotation [31], object detection [32], target segmentation [33], disaster prediction [34,35], identification of oceanic elements [36][37][38], and recognition of dynamic processes [39]. Due to its intelligent and self-learning capabilities, deep learning can overcome the performance limitations of traditional methods in signal modeling and manual feature extraction.…”