  Event-based cameras, also called silicon retinas, potentially revolutionize
computer vision by detecting and reporting significant changes in intensity
asynchronous events, offering extended dynamic range, low latency, and low
power consumption, enabling a wide range of applications from autonomous
driving to longtime surveillance. As an emerging technology, there is a notable
scarcity of publicly available datasets for event-based systems that also
feature frame-based cameras, in order to exploit the benefits of both
technologies. This work quantitatively evaluates a multi-modal camera setup for
fusing high-resolution DVS data with RGB image data by static camera alignment.
The proposed setup, which is intended for semi-automatic DVS data labeling,
combines two recently released Prophesee EVK4 DVS cameras and one global
shutter XIMEA MQ022CG-CM RGB camera. After alignment, state-of-the-art object
detection or segmentation networks label the image data by mapping boundary
boxes or labeled pixels directly to the aligned events. To facilitate this
process, various time-based synchronization methods for DVS data are analyzed,
and calibration accuracy, camera alignment, and lens impact are evaluated.
Experimental results demonstrate the benefits of the proposed system: the best
synchronization method yields an image calibration error of less than 0.90px
and a pixel cross-correlation deviation of1.6px, while a lens with 8mm focal
length enables detection of objects with size 30cm at a distance of 350m
against homogeneous background.
