Abstract
We use Tracking-Learning-Detection algorithm (TLD) [1]-[3] to localize and track objects in images sensed simultaneously by two parallel cameras in order to determine 3D coordinates of the tracked object. TLD method was chosen for its state-of-art performance and high robustness. TLD stores the object to be tracked as a set of 2D grayscale images that is incrementally built. We have implemented the 3D tracking system into a PC, communicating with the Nao humanoid robot [4][5] equipped with a stereo camera head. Experiments evaluating the accuracy of the 3D tracking system are presented. The robot uses feed-forward control to touch the tracked object. The controller is an artificial neural network trained using the error Back-Propagation algorithm. Experiments evaluating the success rate of the robot touching the object are presented.
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Puheim, M., Bundzel, M., Sinčák, P., Madarász, L. (2015). Application of Tracking-Learning-Detection for Object Tracking in Stereoscopic Images. In: Sinčák, P., Hartono, P., Virčíková, M., Vaščák, J., Jakša, R. (eds) Emergent Trends in Robotics and Intelligent Systems. Advances in Intelligent Systems and Computing, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-319-10783-7_35
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DOI: https://doi.org/10.1007/978-3-319-10783-7_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10782-0
Online ISBN: 978-3-319-10783-7
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