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Weihao Qiu

dblp:163/1748 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-6770-9909ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 since 2021Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Legged, aerial and field robots · 40% Face, body and person analysis · 40% Motion planning and robot control · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Face, body and person analysis › human pose estimation
3d pose estimation
0.312018
ACT: An Autonomous Drone Cinematography System for Action Scenes · ICRA 2018
Robotics › Legged, aerial and field robots
aerial robots
0.312018
ACT: An Autonomous Drone Cinematography System for Action Scenes · ICRA 2018
Robotics › Legged, aerial and field robots › aerial robots
drone cinematography
0.312018
ACT: An Autonomous Drone Cinematography System for Action Scenes · ICRA 2018
Computer vision › Face, body and person analysis
human pose estimation
0.312018
ACT: An Autonomous Drone Cinematography System for Action Scenes · ICRA 2018
Robotics › Motion planning and robot control
motion planning
0.312018
ACT: An Autonomous Drone Cinematography System for Action Scenes · ICRA 2018

Methods — techniques the papers use, named apart from their topics

stereo vision · 0.3dynamical camera planning · 0.3
YearPublicationVenuePosition
2023 Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method
Yulan Lin, Mengjie Fan, Weihao Qiu, Justin Wong, Lin Naing, Shabei Xu
Neural Comput. Appl.6
2018 ACT: An Autonomous Drone Cinematography System for Action Scenes
abstract
Drones are enabling new forms of cinematography. Aerial filming via drones in action scenes is difficult because it requires users to understand the dynamic scenarios and operate the drone and camera simultaneously. Existing systems allow the user to manually specify the shots and guide the drone to capture footage, while none of them employ aesthetic objectives to automate aerial filming in action scenes. Meanwhile, these drone cinematography systems depend on the external motion capture systems to perceive the human action, which is limited to the indoor environment. In this paper, we propose an Autonomous CinemaTography system “ACT” on the drone platform to address the above the challenges. To our knowledge, this is the first drone camera system which can autonomously capture cinematic shots of action scenes based on limb movements in both indoor and outdoor environments. Our system includes the following novelties. First, we propose an efficient method to extract 3D skeleton points via a stereo camera. Second, we design a real-time dynamical camera planning strategy that fulfills the aesthetic objectives for filming and respects the physical limits of a drone. At the system level, we integrate cameras and GPUs into the limited space of a drone and demonstrate the feasibility of running the entire cinematography system onboard in real-time. Experimental results in both simulation and real-world scenarios demonstrate that our cinematography system “ACT” can capture more expressive video footage of human action than that of a state-of-the-art drone camera system.
Chong Huang 0005, Fei Gao 0011, Jie Pan 0004, Weihao Qiu, Peng Chen 0008, Xin Yang 0008, Shaojie Shen, Kwang-Ting Cheng
ICRA5