基于轻量化网络的特定目标人体姿态估计算法

HUMAN POSE ESTIMATION ALGORITHM OF SPECIFIC PERSON BASED ON LIGHTWEIGHT NETWORK

  • 摘要: 针对多人场景下对单一目标姿态估计的需求,将 YOLOv5 系列网络与提出的目标选择环节、轻量化 light-duc 模型进行融合,用于特定目标人体姿态估计。该文利用 YOLOv5 网络进行人体框检测;将 DeepSORT 多目标跟踪与条件筛选进行融合,构成目标选择环节用于选出指定目标,设计 light-duc 轻量化模型,完成指定目标人体姿态估计。实验结果表明,所提 light-duc 网络与原网络相比,速度提升了 157%,YOLOv5s 模型与 light-duc 模型结合对单人视频的检测速度提升了 319%。

     

    Abstract: Aiming at the requirement of single person pose estimation in multi-person scene, we combine YOLOv5 series model, object selection and lightweight light-duc model to estimate pose of specific person. YOLOv5 network was used to detect all people in the image. Meanwhile, the object selection composed of DeepSORT multi-object tracking algorithm and criteria-based selection was used to select specific person. The light-duc lightweight network was designed to estimate the pose of specific person. The experimental results show that compared with the original network, the speed of the proposed light-duc network is increased by 157%, combining YOLOv5s with light-duc model, the detection speed of single person video has great improvement, which lead to 319%.

     

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