  Soccer ball detection is identified as one of the critical challenges in the
RoboCup competition. It requires an efficient vision system capable of handling
the task of detection with high precision and recall and providing robust and
low inference time. In this work, we present a novel convolutional neural
network (CNN) approach to detect the soccer ball in an image sequence. In
contrast to the existing methods where only the current frame or an image is
used for the detection, we make use of the history of frames. Using history
allows to efficiently track the ball in situations where the ball disappears or
gets partially occluded in some of the frames. Our approach exploits
spatio-temporal correlation and detects the ball based on the trajectory of its
movements. We present our results with three convolutional methods, namely
temporal convolutional networks (TCN), ConvLSTM, and ConvGRU. We first solve
the detection task for an image using fully convolutional encoder-decoder
architecture, and later, we use it as an input to our temporal models and
jointly learn the detection task in sequences of images. We evaluate all our
experiments on a novel dataset prepared as a part of this work. Furthermore, we
present empirical results to support the effectiveness of using the history of
the ball in challenging scenarios.
