Attentional Object Detection with an Active Multi-Focal Vision System

A biologically inspired foveated attention system in an object detection scenario is proposed. Bottom-up attention uses wide-angle stereo camera data to select a sequence of fixation points. Successive snapshots of high foveal resolution using a telephoto camera enable highly accurate object recognition based on SIFT algorithm. Top-down information is incrementally estimated and integrated using a Kalman-filter, enabling parameter adaptation to changing environments due to robot locomotion. In the experimental evaluation, all the target objects were detected in different backgrounds. Significant improvements in flexibility and efficiency are achieved.

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