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.
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Ball Detection Using Yolo and Mask R-CNN
Semantic Scholar · Computer Science · 2018
Abstract
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.