A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices

TL;DR

This paper introduces an adaptive multi-modal intensity-event system for resource-constrained devices that optimizes object tracking by efficiently compressing and transmitting the most informative data to a host for processing.

Contribution

It proposes a joint intensity-neuromorphic event compression framework with object-driven data acquisition to enhance tracking accuracy under rate constraints.

Findings

Improved MOTA scores using combined modalities.

Effective rate-distortion compression for intensity and events.

Enhanced object detection and tracking performance.

Abstract

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.