A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices
We present a novel adaptive multi-modal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The chip is a computationally resource constrained device acquiring high resolution intensity frames and events, while the ho...
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Zusammenfassung: | We present a novel adaptive multi-modal intensity-event algorithm to optimize
an overall objective of object tracking under bit rate constraints for a
host-chip architecture. The chip is a computationally resource constrained
device acquiring high resolution intensity frames and events, while the host is
capable of performing computationally expensive tasks. We develop a joint
intensity-neuromorphic event rate-distortion compression framework with a
quadtree (QT) based compression of intensity and events scheme. The data
acquisition on the chip is driven by the presence of objects of interest in the
scene as detected by an object detector. The most informative intensity and
event data are communicated to the host under rate constraints, so that the
best possible tracking performance is obtained. The detection and tracking of
objects in the scene are done on the distorted data at the host. Intensity and
events are jointly used in a fusion framework to enhance the quality of the
distorted images, so as to improve the object detection and tracking
performance. The performance assessment of the overall system is done in terms
of the multiple object tracking accuracy (MOTA) score. Compared to using
intensity modality only, there is an improvement in MOTA using both these
modalities in different scenarios. |
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DOI: | 10.48550/arxiv.2105.14164 |