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

• URL: http://arxiv.org/abs/2509.26166v1
• Date: Tue, 30 Sep 2025 12:21:01 GMT
• Title: Beyond Overall Accuracy: Pose- and Occlusion-driven Fairness Analysis in Pedestrian Detection for Autonomous Driving
• Authors: Mohammad Khoshkdahan, Arman Akbari, Arash Akbari, Xuan Zhang,
• Abstract summary: We systematically investigate how variations in the pedestrian pose affect detection performance.<n>We evaluate five pedestrian-specific detectors alongside three general-purpose models.<n>Our findings highlight biases against pedestrians with parallel legs, straight elbows, and lateral views.
• Score: 3.2964881751350177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pedestrian detection plays a critical role in autonomous driving (AD), where ensuring safety and reliability is important. While many detection models aim to reduce miss-rates 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.

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