Name
Abstract | ||
|---|---|---|
This paper proposes an intelligent and fast tracking method for robust trackability against appearance changes. The method hybridizes a correlation-based tracking algorithm operating at hundreds of frames per second (fps) with a deep learning-based recognition algorithm operating at dozens of fps. A prototype intelligent mechanical tracking system was developed by implementing our hybridized tracking algorithm on a 500-fps vision platform. A complex-shaped target can be robustly tracked at the center of the camera view in real time by controlling a pan-tilt active vision system with 500 Hz visual feedback. The tracking performance of our proposed algorithm was verified by showing several experimental results for pre-learned objects, which were quickly manipulated against complex backgrounds. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IROS.2018.8594300 | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Keywords | Field | DocType |
vision platform,visual feedback,pre-learned objects,tracking performance,pan-tilt active vision system,complex-shaped target,hybridized tracking algorithm,prototype intelligent mechanical tracking system,deep learning-based recognition algorithm,fast tracking method,intelligent tracking method,CNN-based object recognition,high-frame-rate target tracking,frequency 500.0 Hz | Computer vision,Active vision system,Computer science,Visualization,Tracking system,Artificial intelligence,Frame rate,Deep learning,Recognition algorithm,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2153-0858 | 978-1-5386-8095-7 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingjun Jiang | 1 | 0 | 0.34 |
| Yihao Gu | 2 | 0 | 0.34 |
| Takeshi Takaki | 3 | 222 | 38.04 |
| Idaku Ishii | 4 | 355 | 64.37 |