发表机构
PediaMed AI; University of Illinois Urbana-Champaign; The Hong Kong Polytechnic University（PediaMed AI; 伊利诺伊大学厄巴纳-香槟分校; 香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该第一届AI儿童挑战赛推出儿童步态视觉分析赛道，设EVGS评分、脑瘫步态模式分类任务，提供对应数据集与真值以推动AI在儿童医疗领域的应用。
AI 中文摘要
第一届AI儿童挑战赛旨在推进计算机视觉与人工智能在儿童医疗、儿童教育及儿科领域的实际应用。2026年CV4CHL版本推出了该领域的首个赛道：儿童步态视觉分析，其核心目标是从关键点序列对儿童步态行为进行细粒度分析，这仍是人类动作识别领域的一大挑战。经验丰富的医生能够区分这些细微差异，但目前尚无针对该领域测试AI模型的相关工作。为填补这一空白，我们引入了数千条涵盖3-16岁各年龄段儿童行走视频的2D关键点序列，对这些视频进行批量分析有望为医疗诊断提供临床相关见解。挑战赛将设置两个问题赛道：爱丁堡视觉步态评分（EVGS）评分、双侧痉挛性脑瘫的步态模式分类，每个赛道均由经委员会认证的儿科医生结合潜在解决方案的价值选定。凭借该类任务的首个可用数据集及各赛道的真值，挑战赛使参与者能够评估自身方案，竞赛结束后将公布最终排名，以促进可复现性并缓解过拟合问题。
英文摘要
The First AI Children Challenge aims to advance real-world applications of computer vision and AI in child healthcare, child education, and pediatrics. The 2026 CV4CHL edition featured the first track in this domain: Children Gait Visual Analysis. The main goal of Children Gait Visual Analysis is the fine-grained analysis of children's gait behaviors from keypoint sequences. This is still a big challenge for human action recognition. Experienced medical doctors can distinguish these subtle nuances, but none of the people test AI models in this domain. To bridge this gap, we introduce thousands of 2D children keypoint sequences walking around videos across various age groups of children (3-16 years old). There is a significant opportunity for batch analysis of these videos to provide clinically relevant insights into medical diagnosis. The Challenge will be launched with two problem tracks: Edinburgh Visual Gait Score (EVGS) Scoring and Classification of Gait Patterns in Bilateral Spastic Cerebral Palsy. Each track is chosen in consultation with board-certified pediatricians based on the value of potential solutions. With the first available dataset for such tasks and ground truth for each track, the challenge enabled participants to evaluate their solutions. Final rankings will be revealed after the competition concludes, fostering reproducibility and mitigating overfitting.
Journal refIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5564-5570. 2026
