Event-driven Proto-object based saliency in 3D space to attract a robot's attention - Supplementary Video

<p>To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their<br> perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features<br> and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that<br> produce synchronous frames. In contrast, event cameras are neuromorphic sensors that loosely mimic the function of the<br> human retina by asynchronously encoding per-pixel brightness changes at very high temporal resolution, thereby providing<br> advantages like high dynamic range, efficiency (thanks to their high degree of signal compression), and low latency. We<br> propose a bio-inspired bottom-up attention model that exploits event-driven sensing to generate depth-based saliency maps<br> that allow a robot to interact with complex visual input. We use event-cameras mounted in the eyes of the iCub humanoid robot<br> to directly extract edge, disparity and motion information. Real-world experiments demonstrate that our system robustly selects<br> salient objects near the robot in the presence of clutter and dynamic scene changes, for the benefit of downstream applications<br> like object segmentation, tracking and robot interaction with external objects.</p>