6 citations · 11 across the 3 of their papers we have counts for
8 papers
Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking
Alan Lukezic, Ziga Trojer, Jiri Matas +1
Visual object tracking has focused predominantly on opaque objects, while transparent object tracking received very little attention. Motivated by the uniqueness of transparent obj…
DAL -- A Deep Depth-aware Long-term Tracker
Yanlin Qian, Alan Lukežič, Matej Kristan +2
The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we prop…
D3S -- A Discriminative Single Shot Segmentation Tracker
Alan Lukežič, Jiří Matas, Matej Kristan
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of tran…
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
Alan Lukežič, Ugur Kart, Jani Käpylä +4
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…
Performance Evaluation Methodology for Long-Term Visual Object Tracking
Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř +2
A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition t…
Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
Ugur Kart, Alan Lukezic, Matej Kristan +2
Standard RGB-D trackers treat the target as an inherently 2D structure, which makes modelling appearance changes related even to simple out-of-plane rotation highly challenging. We…