FAHarris: A Fast and Asynchronous Corner Detector for Event Cameras Recently, the emerging bioinspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous eventbased corner detection method which is called FAHarris. FAHarris consists of several components, including an event filter, a Global Surface of Active Events (GSAE) maintaining unit, a corner candidate selecting unit, and a corner candidate refining unit. The proposed GSAE maintenance algorithm and corner candidate selection algorithm greatly enhance the realtime performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved eventbased Harris detector. Additionally, FAHarris does not require artificially synthesized eventframes and can operate on asynchronous events directly. We implement the proposed method in C and evaluate it on public Event Camera Datasets. The results show that our method achieves approximately 8xc3x97 speedup when compared with previously reported eventbased Harris detector, and with no compromise on the accuracy of performance.

Protoobject based saliency for eventdriven cameras Autonomous robots can rely on attention mechanisms to explore complex scenes and select salient stimuli relevant for behaviour. Stimulus selection should be fast to efficiently allocate available (and limited) computational resources to process in detail a subset of the otherwise overwhelmingly large sensory input. The amount of processing required is a product of the amount of data sampled by a robotxe2x80x99s sensors; while a standard RGB camera produces a fixed amount of data for every pixel of the sensor, an eventcamera produces data only for where there is a contrast change in the field of view, and does so with a lower latency. In this paper, we describe the implementation of a stateoftheart bottomup attention model, based on structuring the visual scene in terms of protoobjects. As an eventcamera encodes different visual information compared to framebased cameras, the original algorithm must be adapted and modified. We find that the eventcameraxe2x80x99s inherent detection of edges removes the need for some early stages of processing in the model. We describe the modifications, compare the eventdriven algorithm to the original, and validate the potential for use on the iCub humanoid robot.