Event-based Object Detection and Tracking for Space Situational Awareness
Saeed Afshar, Andrew P Nicholson, Andre van Schaik, Gregory Cohen

TL;DR
This paper introduces a novel approach to space situational awareness using neuromorphic event-based sensors, presenting a new dataset and evaluating specialized algorithms for detection and tracking of space objects.
Contribution
It provides the first dataset of event-based space imaging, and develops algorithms tailored for detection and tracking in this unique data modality.
Findings
Event-based sensors enable fast, low-power space imaging during day and night.
Developed algorithms show promising accuracy and speed for space object detection and tracking.
The dataset facilitates further research in neuromorphic space imaging.
Abstract
In this work, we present optical space imaging using an unconventional yet promising class of imaging devices known as neuromorphic event-based sensors. These devices, which are modeled on the human retina, do not operate with frames, but rather generate asynchronous streams of events in response to changes in log-illumination at each pixel. These devices are therefore extremely fast, do not have fixed exposure times, allow for imaging whilst the device is moving and enable low power space imaging during daytime as well as night without modification of the sensors. Recorded at multiple remote sites, we present the first event-based space imaging dataset including recordings from multiple event-based sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them. The dataset…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
