论文标题
使用3D姿势估算
A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation
论文作者
论文摘要
最大程度地减少车辆和行人之间的交通事故是智能运输系统中的主要研究目标之一。为了实现目标，行人定向认可和对行人交叉或不交叉意图的预测起着核心作用。当代方法不能保证由于有限的视野，缺乏概括和较高的计算复杂性而导致的令人满意的性能。为了克服这些限制，我们提出了针对两个任务的实时预测行人碰撞警告服务（P2CWS）：行人方向识别（100.53 fps）和意图预测（35.76 fps）。由于提出的独立于站点的特征，我们的框架在多个站点上获得了令人满意的概括。特征提取的中心位于3D姿势估计。 3D姿势分析可以强大而准确地识别行人方向以及对多个地点意图的预测。拟议的视觉框架在TUD数据集的行为识别任务中实现了89.3％的精度，而无需任何培训过程，而在我们的数据集中预测实现新的最新性能的意图预测的精度为91.28％。为了为相应的研究社区做出贡献，我们将源代码公开，可在https://github.com/uehwan/visionforpedestrian上获得
Minimizing traffic accidents between vehicles and pedestrians is one of the primary research goals in intelligent transportation systems. To achieve the goal, pedestrian orientation recognition and prediction of pedestrian's crossing or not-crossing intention play a central role. Contemporary approaches do not guarantee satisfactory performance due to limited field-of-view, lack of generalization, and high computational complexity. To overcome these limitations, we propose a real-time predictive pedestrian collision warning service (P2CWS) for two tasks: pedestrian orientation recognition (100.53 FPS) and intention prediction (35.76 FPS). Our framework obtains satisfying generalization over multiple sites because of the proposed site-independent features. At the center of the feature extraction lies 3D pose estimation. The 3D pose analysis enables robust and accurate recognition of pedestrian orientations and prediction of intentions over multiple sites. The proposed vision framework realizes 89.3% accuracy in the behavior recognition task on the TUD dataset without any training process and 91.28% accuracy in intention prediction on our dataset achieving new state-of-the-art performance. To contribute to the corresponding research community, we make our source codes public which are available at https://github.com/Uehwan/VisionForPedestrian
