activity
20152024
most citedAdaptive Objectness for Object Tracking

35 citations · 98 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024★ 3 cited

IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching

Gangwei Xu, Xianqi Wang, Zhaoxing Zhang +3

Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regi…

astro-ph.IM2024

Reinforcement learning

Sarod Yatawatta

Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operati…

cs.CV2020★ 14 cited

LaSOT: A High-quality Large-scale Single Object Tracking Benchmark

Heng Fan, Hexin Bai, Liting Lin +11

Despite great recent advances in visual tracking, its further development, including both algorithm design and evaluation, is limited due to lack of dedicated large-scale benchmark…

cs.CV2018★ 22 cited

LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking

Heng Fan, Liting Lin, Fan Yang +7

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame…

cs.CV2018★ 23 cited

A Single-shot-per-pose Camera-Projector Calibration System For Imperfect Planar Targets

Bingyao Huang, Samed Ozdemir, Ying Tang +2

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 c…

cs.CV2017★ 1 cited

Planar Object Tracking in the Wild: A Benchmark

Pengpeng Liang, Yifan Wu, Hu Lu +3

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithm…

cs.CV2015★ 35 cited

Adaptive Objectness for Object Tracking

Pengpeng Liang, Chunyuan Liao, Xue Mei +1

Object tracking is a long standing problem in vision. While great efforts have been spent to improve tracking performance, a simple yet reliable prior knowledge is left unexploited…