轻量级行人头部朝向识别网络用于安全的行人-车辆交互

中文摘要 AI 辅助

行人头部朝向识别在自动驾驶中扮演着重要角色，通过提供理解行人注意力及预测潜在过街行为的有价值线索。然而，在真实交通场景中实现可靠识别仍具挑战性，因为行人头部区域通常以低分辨率被捕获。为应对这一挑战，我们提出了一种轻量级的低分辨率头部朝向卷积神经网络（LRHO-CNN），用于行人头部朝向识别。我们通过从多个公开数据集中提取行人头部图像，并手动将其标注为八个朝向类别，构建了一个新数据集。收集的图像经过系统性预处理和增强，以增加数据多样性并更好地表示光照和图像质量的变化。实验分析将LRHO-CNN与三个微调后的基线模型（即ResNet-18、ResNet-34和VGG-16）进行比较。结果表明，LRHO-CNN在所评估的模型中取得了最高的分类准确率。LRHO-CNN还在JAAD和PIE数据集上进行了进一步评估，证明了其在真实交通场景中识别行人头部朝向的有效性，并提供了信息丰富的头部朝向线索，可支持下游的行人行为和意图预测。

英文摘要

Pedestrian head orientation recognition plays an important role in autonomous driving by providing valuable cues for understanding pedestrian attention and anticipating potential crossing behavior. However, reliable recognition in real-world traffic scenes remains challenging because pedestrian head regions are often captured at low resolution. To address this challenge, we propose a lightweight Low-Resolution Head Orientation Convolutional Neural Network (LRHO-CNN) for pedestrian head orientation recognition. We construct a new dataset by extracting pedestrian head images from multiple public datasets and manually annotating them into eight orientation categories. The collected images are systematically preprocessed and augmented to increase data diversity and better represent variations in illumination and image quality. The experimental analysis compares LRHO-CNN with three fine-tuned baseline models, namely ResNet-18, ResNet-34, and VGG-16. The results demonstrate that LRHO-CNN achieves the highest classification accuracy among the evaluated models. LRHO-CNN is further evaluated on the JAAD and PIE datasets, demonstrating its effectiveness in recognizing pedestrian head orientation in real-world traffic scenes and providing informative head-orientation cues that can support downstream pedestrian behavior and intention prediction.

发表机构

• Technische Universität Berlin（柏林工业大学）

机构由 AI 辅助整理，请以论文原文为准。