Handling Camera Movement Constraints in Reinforcement Learning Based Active Object Recognition

In real world scenes, objects to be classified are usually not visible from every direction, since they are almost always positioned on some kind of opaque plane. When moving a camera selectively around those objects for classifying them in an active manner, a hemisphere is fully sufficient for positioning meaningful camera viewpoints. Based on this constraint, this paper addresses the problem of handling planned camera actions which nevertheless lead to viewpoints beyond the plane of that hemisphere. Those actions arise from the uncertainty in the current vertical camera position combined with the view planning method's request of a relative action. The latter is based on an optimized and interpolating query of a knowledge base which is built up in a Reinforcement Learning training phase beforehand. This work discusses the influence of three different, intuitive and optimized, methods for handling invalid action suggestions generated by Reinforcement Learning. Influence is measur...

• Type: Conference
• Year: 2006
• Where: DAGM
• Authors: Christian Derichs, Heinrich Niemann
• Keywords: Camera, DAGM 2006, Image Processing, Meaningful Camera Viewpoints, Reinforcement Learning