Objective Bicycle Occlusion Level Classification using a Deformable Parts-Based Model
This work addresses road safety for cyclists by providing an objective method to classify occlusion levels, which is incremental but important for improving cyclist detection algorithms in autonomous vehicles.
The study tackled the problem of quantifying bicycle occlusion levels for road safety by proposing a novel benchmark using a parts-based detection model, with results showing robust quantification of bicycle visibility and occlusion levels as a significant improvement over subjective methods.
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.