Ball Detection using Yolo and Mask R-CNN
| Autor: | Marina Ivašić-Kos, Matija Buric, Miran Pobar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Feature extraction Detector Cognitive neuroscience of visual object recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Image segmentation Convolutional neural network Object detection action recognition object detection ball detection 020204 information systems 0202 electrical engineering electronic engineering information engineering Ball (bearing) Task analysis 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| DOI: | 10.1109/csci46756.2018.00068 |
| Popis: | Many computer vision applications rely on accurate and fast object detection, and in our case, ball detection serves as a prerequisite for action recognition in handball scenes. We compare the performance of two of the state-of- the-art convolutional neural network-based object detectors for the task of ball detection in non-staged, real-world conditions. The comparison is performed in terms of speed and accuracy measures on a dataset comprising custom handball footage and a sample of images obtained from the Internet. The performance of the models is compared with and without additional training with examples from our dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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