Road obstacle classification with attention windows
Danil V. Prokhorov · 2010
A learning system for detection and classification of road obstacles, such as vehicles and non-vehicles, is proposed which utilizes information from multiple sensors. An advanced range sensor guides a selection of candidate images provided by the camera for subsequent analysis. A competition based learning algorithm is used to distinguish between representations of different obstacles. High classification accuracy is demonstrated in a realistic variety of driving conditions in the presence of intentional data mislabeling in the two-class setup with state-of-art image descriptors.