Beyond Overall Accuracy: Pose- and Occlusion-driven Fairness Analysis in Pedestrian Detection for Autonomous Driving

Pedestrian detection plays a critical role in autonomous driving (AD), where ensuring safety and reliability is important. While many detection models aim to reduce missrates and handle challenges such as occlusion and long-range recognition, fairness remains an underexplored yet equally important concern. In this work, we systematically investigate how variations in the pedestrian pose—including leg status, elbow status, and body orientation—as well as individual joint occlusions, affect detection performance. We evaluate five pedestrian-specific detectors (F2DNet, MGAN, ALFNet, CSP, and Cascade R-CNN) alongside three general-purpose models (YOLOv12 variants) on the EuroCity Persons Dense Pose (ECP-DP) dataset. Fairness is quantified using the Equal Opportunity Difference (EOD) metric across various confidence thresholds. To assess statistical significance and robustness, we apply the Z-test. Our findings highlight biases against pedestrians with parallel legs, straight elbows, and lateral views. Occlusion of lower body joints has a more negative impact on the detection rate compared to the upper body and head. Cascade R-CNN achieves the lowest overall miss-rate and exhibits the smallest bias across all attributes. To the best of our knowledge, this is the first comprehensive pose- and occlusion-aware fairness evaluation in pedestrian detection for AD.