Urban Object Recognition from Informative Local Features

Abstract— Autonomous mobile agents require object recognition for high level interpretation and localization in complex scenes. In urban environments, recognition of buildings might play a dominant role in robotic systems that need object based navigation, that take advantage of visual feedback and multimodal information for self-localization, or that enable association to related information from the identified semantics. We present a new method – the informative local features approach – based on an information theoretic saliency measure that is rapidly derived from a local Parzen window density estimation in feature subspace. From the learning of a decision tree based mapping to informative features, it enables attentive access to discriminative information and thereby significantly speeds up the recognition process. This approach is highly robust with respect to severe degrees of partial occlusion, noise, and tolerant to some changes in scale and illumination. We present pe...

• Type: Conference
• Year: 2005
• Where: ICRA
• Authors: Gerald Fritz, Christin Seifert, Lucas Paletta
• Keywords: ICRA 2005, Object Based Navigation, Object Recognition, Robotics, SIFT Feature Approach