Ball tracking algorithm for mobile devices

Abstract: Object tracking seeks to determine the object size and location in the following video frames, given the appearance and location of the object in the first frame. The object tracking approaches can be divided into categories: online trained trackers and offline trained tracker. First group of trackers is based on handcrafted features like HOG or Color Names. This group is characterised by high inference speed, but struggles from lack of highly deterministic features. On the other hand the second group uses Convolution Neural Networks as features extractors. They generate highly meaningful features, but limit the inference speed and possibility of learning object appearance in the offline phase. The following report investigates the problem of tracking a soccer ball on mobile devices. Keeping in mind the limited computational resources of mobile devices, we propose the fused tracker. At the beginning of the video the simple online trained tracker is fired. As soon as the tracker looses the ball, the more advanced tracer, based on deep neural networks is fired. The fusion allows to speed up the inference time, by using the simple tracker as much as possible, but keeps the tracking success rate high, by using the more advanced tracker after the object is lost by the first tracker. Both quantitative and qualitative experiments demonstrate the validity of this approach.