1 code implementation • 8 Sep 2020 • Heng Fan, Hexin Bai, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Harshit, Mingzhen Huang, Juehuan Liu, Yong Xu, Chunyuan Liao, Lin Yuan, Haibin Ling
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.
no code implementations • CVPR 2019 2019 • Jinzhan Su, Zhe Wang, Chunyuan Liao, Haibin Ling
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
1 code implementation • CVPR 2019 • Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, Haibin Ling
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking.
1 code implementation • 24 Mar 2018 • Bingyao Huang, Samed Ozdemir, Ying Tang, Chunyuan Liao, Haibin Ling
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.
no code implementations • 23 Mar 2017 • Pengpeng Liang, Yifan Wu, Hu Lu, Liming Wang, Chunyuan Liao, Haibin Ling
In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment.
no code implementations • ICCV 2015 • Peiyi Li, Haibin Ling, Xi Li, Chunyuan Liao
In this paper, we propose a real-time 3D hand pose estimation algorithm using the randomized decision forest framework.
no code implementations • 5 Jan 2015 • Pengpeng Liang, Chunyuan Liao, Xue Mei, Haibin Ling
Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.