轻量级模型实现实时行人属性识别，准确率高但稀有属性仍难处理。
Real-time pedestrian attribute recognition with YOLOv8 and ResNet18
- YOLOv8n检测行人，ResNet18分类性别、年龄和61个属性
- 性别识别准确率达99.89%，年龄误差4.23年，多属性准确率89.96%
- 支持25-30帧/秒实时运行，适合监控与交互系统应用
行人属性识别（PAR）为检测到的行人赋予语义标签，在安防、视频检索和人机图形应用中具有价值。本文提出两阶段框架：先用YOLOv8n检测行人，再用基于ResNet18的模型对每个行人裁剪图像进行性别分类、年龄估计及61个二值属性预测。通过语义属性映射合并PETA与PA-100K数据集，构建超过十万张行人图像的统一训练集，同时保留PETA属性空间。在公开测试集上，系统达到99.89%性别分类准确率，4.23年均绝对年龄误差，89.96%多属性准确率，宏F1为36.32%，微F1为58.80%。运行时测量显示在NVIDIA RTX 5060 GPU上可实现25-30 FPS。结果表明，轻量级检测-分类流水线可支持实时PAR，但低宏F1说明稀有属性仍具挑战。
原文摘要 · Abstract (English)
Pedestrian attribute recognition (PAR) assigns semantic labels to detected pedestrians and is useful in surveillance, video retrieval, and human-centered graphics applications. This paper presents a two-stage framework in which YOLOv8n detects pedestrians and ResNet18-based models classify gender, estimate apparent age, and predict 61 binary attributes from each pedestrian crop. PETA and PA-100K are combined through semantic attribute mapping, producing a unified training corpus of more than 100,000 pedestrian images while retaining the PETA attribute space. On the reported test splits, the system obtains 99.89% gender classification accuracy, a 4.23-year apparent-age mean absolute error, and 89.96% multi-attribute accuracy with a 36.32% macro F1-score and 58.80% micro F1-score. Runtime measurements indicate 25-30 FPS on an NVIDIA RTX 5060 GPU. The results show that a lightweight detector-classifier pipeline can support real-time PAR, while low macro F1 indicates that rare attributes remain challenging.
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