Predicting Learning Tracking: A comparative study
Wafae Mrabti, Kaoutar Baibai, Benaïssa Bellach, Rachid Oulad Haj Thami, Hamid Tairi · 2018
In this paper, we aim to carry out a brief study of a new tracking system and the results of existing tracking systems including Compressive Tracking algorithm (CT), Fragment Tracker (FRAG), Multiple Instance Learning Tracker (MIL), and Tracking Learning Detection algorithm (TLD). For this purpose, we present the principles of those tracking systems. Then, we test them on several datasets that contain challenging attributions. Finally, we evaluate them qualitatively and quantitatively by two widely used criterions: the Average Center Error (ACE) and the Average Overlap Error (AOE). We conclude our paper by discussing the obtained results and the future works in this field.