  Pedestrian detection is among the most safety-critical features of driver
assistance systems for autonomous vehicles. One of the most complex detection
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 current pedestrian detection benchmarks provide annotation
for partial occlusion to assess algorithm performance in these scenarios,
however each benchmark varies greatly in their definition of the occurrence and
severity of occlusion. In addition, current occlusion level annotation methods
contain a high degree of subjectivity by the human annotator. This can lead to
inaccurate or inconsistent reporting of an algorithm's detection performance
for partially occluded pedestrians, depending on which benchmark is used. This
research presents a novel, objective method for pedestrian occlusion level
classification for ground truth annotation. Occlusion level classification is
achieved through the identification of visible pedestrian keypoints and through
the use of a novel, effective method of 2D body surface area estimation.
Experimental results demonstrate that the proposed method reflects the
pixel-wise occlusion level of pedestrians in images and is effective for all
forms of occlusion, including challenging edge cases such as self-occlusion,
truncation and inter-occluding pedestrians.
