  In this paper, we provide an extensive evaluation of the performance of local
descriptors for tracking applications. Many different descriptors have been
proposed in the literature for a wide range of application in computer vision
such as object recognition and 3D reconstruction. More recently, due to fast
key-point detectors, local image features can be used in online tracking
frameworks. However, while much effort has been spent on evaluating their
performance in terms of distinctiveness and robustness to image
transformations, very little has been done in the contest of tracking. Our
evaluation is performed in terms of distinctiveness, tracking precision and
tracking speed. Our results show that binary descriptors like ORB or BRISK have
comparable results to SIFT or AKAZE due to a higher number of key-points.
