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Dr. Gedikli, Suat
Researcher
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Table of ContentsResearch TopicsCurrent Research Projects Former Research Projects |
Research Topics
- Probabilistic Model Fitting
- Model Fitting
- Tracking Soccer Players
- Model-Based Image Interpretation
- Data Mining / Knowledge Discovery
- Visual SLAM
Current Research Projects
- ASPOGAMO : The research project 'Sensor-based, Automatic Analysis of Football Games' is an ambitious, mid-term research project that studies the automation of these tasks. The main objectives of the project are (1) the investigation of novel computational mechanisms that enable computer systems to recognize intentional activities, (2) the development of an integrated software system to automate game interpretation and analysis, and (3) the demonstration of the impact of automated game analysis on application areas, such as sport science, football coaching, and sports entertainment.
- Face Image Analysis : Recent progress in the field of Computer Vision allows intuitive interaction via gesture or facial expressions between humans and technical systems. This project aims at determining facial expressions from camera images in real-time. Model-based image interpretation techniques have proven to be a successful method for extracting such high-level information from single images and image sequences. Models rely on a priori knowledge to reduce the large amount of image data to a small number of model parameters. This representation of the image content facilitates and accelerates the subsequent interpretation task. In order to extract high-level information, model parameters have to be estimated that best describe the face within a given image. However, correctly estimated model parameters forms the basis of various more applications such as gaze detection or gender estimation. Our demonstrator for facial expression estimation has been presented at several events with political audience and on TV.
Former Research Projects
- Agilo : Robotic soccer has become a standard 'real-world' testbed for autonomous multi robot control. In robot soccer (mid-size league) two teams of four autonomous robots --- one goal keeper and three field players --- play soccer against each other. The soccer field is four by nine meters big. The key characteristics of mid-size robot soccer are that the robots are completely autonomous. Consequently, all sensing and all action selection is done onboard of the individual robots. Skillful play requires our robots to recognize objects, such as other robots, field lines, and goals, and even entire game situations. In the AGILO project we investigate how probabilistic visuomotoric autonomous robot controllers that are capable of learning can meet these challenges. The AGILO robot controllers employ game state estimation and situated action selection based on automatically learned control mechanisms.
