Active Structured Learning for High-Speed Object Detection
Max Planck Institute for Biological Cybernetics, Tubingen, Germany
In Proceedings of the 31st DAGM Symposium on Pattern Recognition (2009), pp. 221-231.
High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system that can adapt to arbitrary objects and a wide range of lighting conditions is a challenging problem, especially if hard real-time constraints apply like in robotics scenarios. In this work, we introduce a method for learning a discriminative object tracking system based on the recent structured regression framework for object localization. Using a kernel function that allows fast evaluation on the GPU, the resulting system can process video streams at speed of 100 frames per second or more.
November 19, 2010 by hgpu
