Event-Based Noise Filtration with Point-of-Interest Detection and Tracking for Space Situational Awareness

Abstract

This paper explores an asynchronous noise-suppression technique to be used in conjunction with asynchronous Gaussian blob tracking on dynamic vision sensor (DVS) data, specifically for space-based object tracking. The technique presented treats each sensor pixel as a spiking cell whose activity can be filtered out of the resulting sensor event stream by user-defined threshold values. In the space environment, radiation effects can introduce both transient and persistent noise into the DVS event stream. For space applications, targets of interest may be no larger than a single pixel and can be indistinguishable from sensor noise. In this paper, the asynchronous approach is experimentally compared to a conventional approach applied to reconstructed frame data for both performance and accuracy metrics. The results of this research show that the asynchronous approach can produce comparable or superior tracking accuracy while also drastically reducing the execution time of the process by seven times on average.