What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Robot, Mobile robot and Detector.
His research on Artificial intelligence frequently connects to adjacent areas such as Trajectory.
His studies deal with areas such as Classifier, AdaBoost and Robustness as well as Computer vision.
His Robot research is mostly focused on the topic Motion planning.
His Mobile robot research includes themes of Motion, Machine learning and Human–computer interaction.
While the research belongs to areas of Detector, he spends his time largely on the problem of RGB color model, intersecting his research to questions surrounding Point cloud, Histogram, Histogram of oriented gradients, Data set and Tracking system.
His most cited work include:
- People detection in RGB-D data (320 citations)
- Using Boosted Features for the Detection of People in 2D Range Data (306 citations)
- Robox at expo.02: A Large Scale Installation of Personal Robots (205 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Robot, Mobile robot, Computer vision and Human–computer interaction.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
His study in the field of Social robot also crosses realms of Key.
His work deals with themes such as Probabilistic logic and Motion planning, which intersect with Mobile robot.
His Computer vision study combines topics from a wide range of disciplines, such as Kalman filter, Detector, Odometry and Robustness.
His Human–computer interaction study combines topics in areas such as Interactivity, Multimedia and Embedded system.
He most often published in these fields:
- Artificial intelligence (68.15%)
- Robot (51.85%)
- Mobile robot (41.48%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (68.15%)
- Motion planning (16.30%)
- Robotics (17.78%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Motion planning, Robotics, Robot and Motion.
His work carried out in the field of Artificial intelligence brings together such families of science as Trajectory, Computer vision and Pattern recognition.
His Computer vision study frequently links to other fields, such as Human–robot interaction.
He interconnects Mathematical optimization and Model predictive control in the investigation of issues within Motion planning.
His Robot research incorporates elements of State, Data science and Taxonomy.
Kai O. Arras combines subjects such as Computer engineering and Benchmark with his study of Mobile robot.
Between 2018 and 2021, his most popular works were:
- Human motion trajectory prediction: a survey: (113 citations)
- Human Motion Trajectory Prediction: A Survey (40 citations)
- Multi-Path Learning for Object Pose Estimation Across Domains (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Robot, Trajectory, Computer vision and Robotics.
His work on Pose and Object detection as part of general Artificial intelligence research is often related to Sampling, Encoder and Scale, thus linking different fields of science.
His studies in Robot integrate themes in fields like Mathematical optimization and Taxonomy.
His Trajectory study integrates concerns from other disciplines, such as Human motion, Data science and Motion prediction.
His work in the fields of Computer vision, such as Image, intersects with other areas such as Metric, Basis and Truncation.
His Robotics research incorporates themes from Probabilistic logic and Holonomic.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
