Morphological, Object Detection Framework for Embedded, Event-based Sensing

Abstract

This paper presents a high-speed, object detection algorithm that leverages data from an event-based camera and a spike-based, cellular neural network framework for morphological processing. Event-based data flows into the algorithm in a time-serial, asynchronous fashion, but the algorithm and subsequent architecture description lends itself towards a parallel approach. A cellular neural-network (CNN) is composed of multimodal processing elements that provide the means to spatiotemporally filter event data, but also are used to apply a cascade of piece-wise linear functions in a synchronous fashion. When applied in succession, these morphological operations form object “blobs”, produce shape skeletons, and place centroids. Over an event stream, this rapid centroid placement provides a means to perform low-latency object detection in an embedded framework. Using processing intervals of 25ms and assuming a clock of 100 MHz, a computational latency of around 5.5μs is incurred and an estimated 161uW is consumed by the morphological algorithm thus providing a promising solution for event-based, embedded processing.