High-frame-rate Target Tracking with CNN-based Object Recognition

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.