Robust and adaptive keypoint-based object tracking
| Autor: | Masatoshi Ishikawa, Hedvig Kjellström, Niklas Bergström, Alessandro Pieropan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science 020207 software engineering 02 engineering and technology Computer Science Applications Human-Computer Interaction Activity recognition Hardware and Architecture Control and Systems Engineering Robustness (computer science) Video tracking 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Pose Software |
| Zdroj: | Advanced Robotics. 30:258-269 |
| ISSN: | 1568-5535 0169-1864 |
| Popis: | Object tracking is a fundamental ability for a robot; manipulation as well as activity recognition relies on the robot being able to follow objects in the scene. This paper presents a tracker that adapts to changes in object appearance and is able to re-discover an object that was lost. At its core is a keypoint-based method that exploits the rigidity assumption: pairs of keypoints maintain the same relations over similarity transforms. Using a structured approach to learning, it is able to incorporate new appearances in its model for increased robustness. We show through quantitative and qualitative experiments the benefits of the proposed approach compared to the state of the art, even for objects that do not strictly follow the rigidity assumption. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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