  Gathering data and identifying events in various traffic situations remains
an essential challenge for the systematic evaluation of a perception system's
performance. Analyzing large-scale, typically unstructured, multi-modal, time
series data obtained from video, radar, and LiDAR is computationally demanding,
particularly when meta-information or annotations are missing. We compare
Optical Flow (OF) and Deep Learning (DL) to feed computationally efficient
event detection via space-filling curves on video data from a forward-facing,
in-vehicle camera. Our first approach leverages unexpected disturbances in the
OF field from vehicle surroundings; the second approach is a DL model trained
on human visual attention to predict a driver's gaze to spot potential event
locations. We feed these results to a space-filling curve to reduce
dimensionality and achieve computationally efficient event retrieval. We
systematically evaluate our concept by obtaining characteristic patterns for
both approaches from a large-scale virtual dataset (SMIRK) and applied our
findings to the Zenseact Open Dataset (ZOD), a large multi-modal, real-world
dataset, collected over two years in 14 different European countries. Our
results yield that the OF approach excels in specificity and reduces false
positives, while the DL approach demonstrates superior sensitivity. Both
approaches offer comparable processing speed, making them suitable for
real-time applications.
