To mitigate the challenges arising from partial occlusion in human pose keypoint based pedestrian detection methods , we present a novel pedestrian pose keypoint completion method called the separation and dimensionality reduction-based generative adversarial imputation networks (SDR-GAIN) . Firstly, we utilize OpenPose to estimate pedestrian poses in images. Then, we isolate the head and torso keypoints of pedestrians with incomplete keypoints due to occlusion or other factors and perform dimensionality reduction to enhance features and further unify feature distribution. Finally, we introduce two generative models based on the generative adversarial networks (GAN) framework, which incorporate Huber loss, residual structure, and L1 regularization to generate missing parts of the incomplete head and torso pose keypoints of partially occluded pedestrians, resulting in pose completion. Our experiments on MS COCO and JAAD datasets demonstrate that SDR-GAIN outperforms basic GAIN framework, interpolation methods PCHIP and MAkima, machine learning methods k-NN and MissForest in terms of pose completion task. In addition, the runtime of SDR-GAIN is approximately 0.4ms, displaying high real-time performance and significant application value in the field of autonomous driving.

翻译：为缓解基于人体姿态关键点的行人检测方法中因局部遮挡带来的挑战，我们提出了一种名为分离降维生成对抗插补网络（SDR-GAIN）的新型行人姿态关键点补全方法。首先，我们利用OpenPose估计图像中的行人姿态；其次，针对因遮挡等因素导致关键点不完整的行人，分离其头部与躯干关键点，通过降维增强特征并统一特征分布；最后，基于生成对抗网络（GAN）框架引入两种生成模型，结合Huber损失、残差结构与L1正则化，对部分遮挡行人的不完整头部与躯干姿态关键点进行缺失部分生成，实现姿态补全。在MS COCO与JAAD数据集上的实验表明，SDR-GAIN在姿态补全任务中优于基础GAIN框架、插值方法PCHIP与MAkima、机器学习方法k-NN与MissForest。此外，SDR-GAIN的运行时间约为0.4毫秒，展现出高实时性，在自动驾驶领域具有显著应用价值。