MOVING OBJECT DETECTION WITH MEMRISTIVE CROSSBAR ARRAYS

Abstract

This thesis is dedicated to the hardware implementation of a novel moving object detection algorithm. Proposed circuit includes several stages, each of which implements a particular step of the algorithm. Four higher bit planes are extracted from a grayscale image and stored in memristive crossbar arrays, and the respective bit planes are compared via memristive threshold logic gates in XOR configuration. In the next stage, compared bit planes are combined by weighted summation, with a highest weight assigned to MSB plane and smaller weights for less significant bit planes. After summation stage, obtained grayscale image is thresholded to obtain binary image. The last stage is implemented via memristive content-addressable memory array, which serves two purposes. It is used as a long-term memory in comparison to crossbar arrays, which serve as a short-term memory of proposed circuit. Content-addressable memory is updated based on the row-by-row difference between first and second pair of frames processed by previous stages. It also allows for analysis of object movement direction and velocity by observing the row capacitors’ discharge. Simulations show that accuracy of proposed circuit operation is increased with the larger array size. Delay analysis of the circuit is carried out, power and area calculations show that proposed circuit is a viable candidate as a co-processing operator for existing image sensors.