Object Detection Using Synthesized Data (CROSBI ID 686460)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Burić, Matija ; Paulin, Goran ; Ivašić-Kos, Marina
engleski
Object Detection Using Synthesized Data
Successful object detection, using CNN, requires lots of well-annotated training data which is currently not available for action recognition in the handball domain. Augmenting real-world image dataset with synthesized images is not a novel approach, but the effectiveness of the creation of such a dataset and the quantities of generated images required to improve the detection can be. Starting with relatively small training dataset, by combining traditional 3D modeling with proceduralism and optimizing generator-annotator pipeline to keep rendering and annotating time under 3 FPS, we achieved 3x better detection results, using YOLO, while only tripling the training dataset.
Object Detection ; Convolutional Neural Network ; YOLO ; Synthesized Data ; Sports ; Handball
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Podaci o prilogu
110-124.
2019.
objavljeno
Podaci o matičnoj publikaciji
Podaci o skupu
11th International Conference ICT Innovations
predavanje
17.10.2019-19.10.2019
Ohrid, Sjeverna Makedonija