Object detection in sports videos

Object detection is commonly used in many computer vision applications. In our case, we need to apply the object detector as a prerequisite for action recognition in handball scenes. Object detection, to be successful for this task, should be as accurate as possible and should be able to deal with a different number of objects of various sizes, partially occluded, with bad illumination and deal with cluttered scenes. The aim of this paper is to provide an overview of the current state-of-the-art detection methods that rely on convolutional neural networks (CNNs) and test their performance on custom video sports materials acquired during handball training and matches. The comparison of the detector performance in different conditions will be given and discussed.