Pre-processor for bioinspired optical flow models: a customizable hardware implementation

Nature has optimized the processing of visual information, especially in primates. The estimation of optical flow is a complex task that gives information about ego-motion, and permits tracking objects from a given scene. The multi-channel spatio-temporal filtering required to detect motion is suitable for a parallel implementation on reconfigurable circuitry. We detail here the design of a neuromorphic FPGA implementation of the pre-processing stages for optical flow estimation that permits highly parallel real-time filtering