Active Structured Learning for High-Speed Object Detection

Christoph H. Lampert, Jan Peters
Max Planck Institute for Biological Cybernetics, Tubingen, Germany
In Proceedings of the 31st DAGM Symposium on Pattern Recognition (2009), pp. 221-231.


   title={Active structured learning for high-speed object detection},

   author={Lampert, C. and Peters, J.},

   journal={Pattern Recognition},





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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.
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