• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems >Binocular Human Body Attitude Distance Localization Recognition Algorithm Based on Dual Convolution
【24h】

Binocular Human Body Attitude Distance Localization Recognition Algorithm Based on Dual Convolution

机译：基于双卷积的双目人体姿态距离定位识别算法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Objective: An algorithm based on real-time accurate target recognition and distance location for VAVs, the existing target location and discrimination methods often fail to meet practical requirements. Method: The image information collected under the common single camera can only obtain two-dimensional information, and the relative distance of the camera based on the target cannot be obtained. However, the commonly used dual camera-based distance acquisition algorithm is too complicated, not stable enough, and requires developers to have higher the level of knowledge, the high threshold for development, and the difficulty of application. Therefore, this paper proposes to train the feature extraction network based on the two-channel Darknet-53 basic structure through the dual camera under the human body posture recognition image dataset, and initialize the YOLOv2 network with its parameters, and to train the human body position in the human body posture image, relative distance, and category. Result: Experimental results verify that the human body position and category recognition of human posture using this method improves the recognition accuracy by 3.83% and 4.81% compared with the single-convolution chain, and the accuracy of the target-based relative distance is achieved 65.21%. Conclusion: The algorithm can be effectively applied to the UAV to quickly recognize the human body posture and obtain a better recognition effect to meet the real-time demand.
机译：目的：基于实时准确的目标识别和VAV的距离位置的算法，现有的目标位置和识别方法通常无法满足实际要求。方法：在公共单个相机下收集的图像信息只能获得二维信息，并且不能获得基于目标的相机的相对距离。但是，常用的基于双相机的距离采集算法太复杂，不够稳定，并且需要开发人员具有更高的知识水平，开发的高阈值以及应用的难度。因此，本文建议通过人体姿势识别图像数据集下的双相机基于双通道Darknet-53基本结构训练特征提取网络，并以其参数初始化YOLOV2网络，并训练人体在人体姿势图像，相对距离和类别中的位置。结果：实验结果验证了使用该方法的人体位置和人体姿势的类别识别，与单卷积链相比，将识别精度提高了3.83％和4.81％，并实现了目标的相对距离的准确性65.21 ％。结论：该算法可以有效地应用于无人机，以快速识别人体姿势，获得更好的识别效果，以满足实时需求。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号