  Robust detection of vulnerable road users is a safety critical requirement
for the deployment of autonomous vehicles in heterogeneous traffic. One of the
most complex outstanding challenges is that of partial occlusion where a target
object is only partially available to the sensor due to obstruction by another
foreground object. A number of leading pedestrian detection benchmarks provide
annotation for partial occlusion, however each benchmark varies greatly in
their definition of the occurrence and severity of occlusion. Recent research
demonstrates that a high degree of subjectivity is used to classify occlusion
level in these cases and occlusion is typically categorized into 2 to 3 broad
categories such as partially and heavily occluded. This can lead to inaccurate
or inconsistent reporting of pedestrian detection model performance depending
on which benchmark is used. This research introduces a novel, objective
benchmark for partially occluded pedestrian detection to facilitate the
objective characterization of pedestrian detection models. Characterization is
carried out on seven popular pedestrian detection models for a range of
occlusion levels from 0-99%, in order to demonstrate the efficacy and increased
analysis capabilities of the proposed characterization method. Results
demonstrate that pedestrian detection performance degrades, and the number of
false negative detections increase as pedestrian occlusion level increases. Of
the seven popular pedestrian detection routines characterized, CenterNet has
the greatest overall performance, followed by SSDlite. RetinaNet has the lowest
overall detection performance across the range of occlusion levels.
