Tracking from a moving platform with the Dynamic Vision Sensor

Joseph Cox; Nicholas Morley

doi:10.1117/12.2518761

13 May 2019 Tracking from a moving platform with the Dynamic Vision Sensor

Joseph Cox, Nicholas Morley

Proceedings Volume 10990, Computational Imaging IV; 109900O (2019) https://doi.org/10.1117/12.2518761
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

The Dynamic Vision Sensor (DVS) is an imaging sensor that processes the incident irradiance image and outputs temporal log irradiance changes in the image, such as those generated by moving target(s) and/or the moving sensor platform. From a static platform, this enables the DVS to cancel out background clutter and greatly decrease the sensor bandwidth required to track temporal changes in a scene. However, the sensor bandwidth advantage is lost when imaging a scene from a moving platform due to platform motion causing optical flow in the background. Imaging from a moving platform has been utilized in many recently reported applications of this sensor. However, this approach inherently outputs background clutter generated from optical flow, and as such this approach has limited spatio-temporal resolution and is of limited utility for target tracking applications. In this work we present a new approach to moving target tracking applications with the DVS. Essentially, we propose modifying the incident image to cancel out optical flow due to platform motion, thereby removing background clutter and recovering the bandwidth performance advantage of the DVS. We propose that such improved performance can be accomplished by integrating a hardware tracking and stabilization subsystem with the DVS. Representative simulation scenarios are used to quantify the performance of the proposed approach to clutter cancellation and improved sensor bandwidth.

Conference Presentation

Citation Download Citation

Joseph Cox and Nicholas Morley "Tracking from a moving platform with the Dynamic Vision Sensor", Proc. SPIE 10990, Computational Imaging IV, 109900O (13 May 2019); https://doi.org/10.1117/12.2518761

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