Mendeley
TY  _  JOUR
ID  -  1400061224247
TI  -  An efficient Two Pathways Deep Architecture for Soccer Goal Recognition towards Soccer Highlight Summarization
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  Zangane Amirhosein
AU  -  Jampour Mehdi
AU  -  Layeghi Kamran
AD  -  
AD  -  
AD  -  دانشگاه آزاد
AD  -  
AD  -  دانشگاه آزاد
Y1  -  2021
PY  -  2021
VL  -  49
IS  -  
SP  -  103
EP  -  114
KW  -   Dual-Path Deep Learning Architecture
KW  -   Feature Combination
KW  -   Deep VGG
KW  -   Classic Features
KW  -   Common Architecture
DO  -  
N2  -  In this paper, an automated method has been presented using a dual-path deep learning architecture model for the problem of soccer video analysis and it emphasizes the gate recognition as one of the most important elements of the goal event that is the most important soccer game event. The proposed architecture is considered as an extended form of the VGG 13-layer model in which a dual-path architectural model has been defined. For recognizing the gate in the first path using the proposed architectural model, the model is trained by the training dataset. But in the second path, the training dataset is first examined by a screening system and the best images containing features different from the features of the first path are selected. In another word, features of a network similar to the first path, but after passing through the screening system are generated in the second path. Afterwards, the feature vectors generated in two paths are combined to create a global feature vector, thus covering different spaces of the gate recognition problem. Different evaluations have been performed on the presented method. The evaluation results represent the improved accuracy of gate recognition using the proposed dual-path architectural model in comparison to the basic model. A comparison of proposed method with other existing outcomes also represents the improved accuracy of the proposed method in comparison to the published results.
UR  - rimag.ir/en/Article/24247
L1  - rimag.ir/en/Article/Download/24247
ER  -  
