Tracking system for a soccer robot game using neural network
Date of Publication
Master of Science in Computer Science
College of Computer Studies
Defense Panel Chair
Elmer P. Dadios
Defense Panel Member
The conventional tracking algorithm lacks the capability to learn. Approaches like the use of neural network, which has learning capability, may be incorporated to the tracking algorithm to take advantage of previously estimated pose. Neural network approach may be investigated in terms of speed and accuracy by comparing it with the conventional tracking algorithm. This research develops a pose estimation algorithm using neural network as its paradigm. Pose estimation is concerned with finding an object's position and orientation. There are several approaches in handling pose estimation, and one of them is through the use of neural network. Neural network, with its learning capability, can take advantage of previously estimated pose and use this for future estimation. The pose estimation algorithm will be tested in the game of soccer robots, specifically the Micro-Robot World Cup Soccer Tournament (MiroSot).
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
1 v (various foliations) ; 28 cm.
Neural networks (Computer science); Robotics; Games; Soccer; Computer vision; Robot vision; Micro League Football (Game)
Pantola, A. (2001). Tracking system for a soccer robot game using neural network. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/2625