A pipeline for evaluation of keypoint-based bounding boxes for multi-scale pedestrians
Author(s)
Ahmed, Sarfraz
Huda, M Nazmul
Saha, Chitta
Quddus, Mohammed
Type
Journal Article
Abstract
Pedestrians account for approximately 25% of traffic accidents, many of which can be prevented using autonomous driving systems (ADS). Although pedestrian detection has advanced significantly, intent prediction still lags behind human perception. A key challenge is predicting the intent of smaller pedestrians, who are harder to detect and analyse using 2D pose estimation techniques because of their tendency to blend into the background. However, human joint keypoints (e.g., head, shoulders, elbows, and knees) can reliably predict pedestrian movement, such as gait, limb motion, and head orientation. This paper introduces the Keypoint Evaluation (KeyEval) pipeline, a new technique for generating high-quality pedestrian keypoints using a 2D pose estimator. Leveraging ground-truth bounding box annotations from the JAAD and PIE datasets, we assess keypoint accuracy and apply state-of-the-art fine-tuning, achieving a 19% improvement in average precision (76%) over the baseline. This suggests KeyEval can enhance predictions of pedestrian intent—crossing, waiting, or changing direction—particularly for smaller pedestrians. The KeyEval pipeline can be seamlessly integrated into ADS to proactively reduce vehicle-pedestrian accidents, as demonstrated in our previous work.
Date Issued
2026-03-11
Date Acceptance
2026-02-24
Citation
IEEE Access, 2026, 14, pp.37233-37244
ISSN
2169-3536
Publisher
IEEE
Start Page
37233
End Page
37244
Journal / Book Title
IEEE Access
Volume
14
Copyright Statement
© 2026 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Publication Status
Published
Date Publish Online
2026-03-06