Replacing the human driver: An objective benchmark for occluded pedestrian detection

Abstract

Early detection of vulnerable road users is a crucial requirement for autonomous vehicles to meet and exceed the object detection capabilities of human drivers. 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. Research demonstrates that a high degree of subjectivity is used to classify occlusion level in these cases and occlusion is typically categorized into 2–3 broad categories such as “partially” and “heavily” occluded. In addition, many pedestrian instances are impacted by multiple inhibiting factors which contribute to non-detection such as object scale, distance from camera, lighting variations and adverse weather. This can lead to inaccurate or inconsistent reporting of detection performance for partially occluded pedestrians 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% to demonstrate the impact of progressive levels of partial occlusion on pedestrian detectability. Results show that the proposed benchmark provides more objective, fine grained analysis of pedestrian detection algorithms than the current state of the art.