  Tracking sports players is a widely challenging scenario, specially in
single-feed videos recorded in tight courts, where cluttering and occlusions
cannot be avoided. This paper presents an analysis of several geometric and
semantic visual features to detect and track basketball players. An ablation
study is carried out and then used to remark that a robust tracker can be built
with Deep Learning features, without the need of extracting contextual ones,
such as proximity or color similarity, nor applying camera stabilization
techniques. The presented tracker consists of: (1) a detection step, which uses
a pretrained deep learning model to estimate the players pose, followed by (2)
a tracking step, which leverages pose and semantic information from the output
of a convolutional layer in a VGG network. Its performance is analyzed in terms
of MOTA over a basketball dataset with more than 10k instances.
