Abstract
Deep Learning Trackers Review and Challenge
Highlights
Visual tracking has numerous realistic applications in navigation, surveillance, robotics, augmented reality, to name a few
This dataset includes 100 challenging video clips annotated with different attributes, such as Illumination Variation (IV), Scale Variation (SV), Occlusion (OCC), Deformation (DEF), Motion Blur (MB), Fast Motion (FM), In-Plane Rotation (IPR), Outof-Plane Rotation (OPR), Out-of-View (OV), Background Clutters (BC), and Low Resolution (LR)
The major difference between VOT2015 and OTB-100 is that the VOT2015 challenge provides a reinitialization protocol
Summary
Visual tracking has numerous realistic applications in navigation, surveillance, robotics, augmented reality, to name a few. It is still a challenging task to develop a robust tracker to handle the complex scenes. Traditional trackers usually focus on developing robust appearance model from the perspectives of hand-crafted features, online learning algorithms or both. Some milestones include IVT [Ross, Lim, Lin et al (2008)], MIL [Babenko, Yang, Belongie et al (2011)], TLD [Kalal, Mikolajczyk and Matas (2012)], APGL1 [Bao, Wu, Ling et al. JIHPP. The reports on large-benchmark evaluations (both OTB-100 [Wu, Lim and Yang (2015)], TC128 [Liang, Blasch and Ling (2015)] and VOT2015 [Kristan, Matas, Leonardis et al (2015)]) suggest that the performance of these traditional algorithms is far from the requirement of realistic applications
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