Objective Bicycle Occlusion Level Classification using a Deformable Parts-Based Model

TL;DR

This paper introduces a new computer vision benchmark for classifying bicycle occlusion levels, using a parts-based detection model to objectively quantify bicycle visibility and occlusion, aiding autonomous vehicle safety.

Contribution

It presents a novel benchmark and method for objectively measuring bicycle occlusion levels, improving upon subjective assessment methods in the field.

Findings

Model robustly quantifies bicycle occlusion levels

Significant improvement over subjective methods

Facilitates accurate cyclist detection evaluation

Abstract

Road safety is a critical challenge, particularly for cyclists, who are among the most vulnerable road users. This study aims to enhance road safety by proposing a novel benchmark for bicycle occlusion level classification using advanced computer vision techniques. Utilizing a parts-based detection model, images are annotated and processed through a custom image detection pipeline. A novel method of bicycle occlusion level is proposed to objectively quantify the visibility and occlusion level of bicycle semantic parts. The findings indicate that the model robustly quantifies the visibility and occlusion level of bicycles, a significant improvement over the subjective methods used by the current state of the art. Widespread use of the proposed methodology will facilitate accurate performance reporting of cyclist detection algorithms for occluded cyclists, informing the development of more robust vulnerable road user detection methods for autonomous vehicles.