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Found 7 papers, 3 papers with code
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement
In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result.
Ranked #14 on
Face Alignment
on 300W
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking.
A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets
Existing camera-projector calibration methods typically warp feature points from a camera image to a projector image using estimated homographies, and often suffer from errors in camera parameters and noise due to imperfect planarity of the calibration target.
Planar Object Tracking in the Wild: A Benchmark
In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment.
3D Hand Pose Estimation Using Randomized Decision Forest With Segmentation Index Points
In this paper, we propose a real-time 3D hand pose estimation algorithm using the randomized decision forest framework.
Adaptive Objectness for Object Tracking
Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.
