Visual attention for object recognition in spatial 3D data

In this paper, we present a new recognition system for the fast detection and classification of objects in spatial 3D data. The system consists of two main components: A biologically motivated attention system and a fast classifier. Input is provided by a 3D laser scanner, mounted on an autonomous mobile robot, that acquires illumination independent range and reflectance data. These are rendered into images and fed into the attention system that detects regions of potential interest. The classifier is applied only to a region of interest, yielding a significantly faster classification that requires only 30% of the time of an exhaustive search. Furthermore, both the attention and the classification system benefit from the fusion of the bi-modal data, considering more object properties for the detection of regions of interest and a lower false detection rate in classification.