Smart Vision & and Sensing
Department of System Cybernetics, Graduate School of Advanced Science and Engineering
High-frame-rate Target Tracking with CNN-based Object Recognition
We propose 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.
|AVI movie(23.3 MB)
- Mingjun Jiang, Yihao Gu, Takeshi Takaki, and Idaku Ishii, High-frame-rate Target Tracking with CNN-based Object Recognition, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 599-606, 2018.