  The human eye consists of two types of photoreceptors, rods and cones. Rods
are responsible for monochrome vision, and cones for color vision. The number
of rods is much higher than the cones, which means that most human vision
processing is done in monochrome. An event camera reports the change in pixel
intensity and is analogous to rods. Event and color cameras in computer vision
are like rods and cones in human vision. Humans can notice objects moving in
the peripheral vision (far right and left), but we cannot classify them (think
of someone passing by on your far left or far right, this can trigger your
attention without knowing who they are). Thus, rods act as a region proposal
network (RPN) in human vision. Therefore, an event camera can act as a region
proposal network in deep learning Two-stage object detectors in deep learning,
such as Mask R-CNN, consist of a backbone for feature extraction and a RPN.
Currently, RPN uses the brute force method by trying out all the possible
bounding boxes to detect an object. This requires much computation time to
generate region proposals making two-stage detectors inconvenient for fast
applications. This work replaces the RPN in Mask-RCNN of detectron2 with an
event camera for generating proposals for moving objects. Thus, saving time and
being computationally less expensive. The proposed approach is faster than the
two-stage detectors with comparable accuracy
