基于多相机融合的移动行人跟踪
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作者单位:

武汉科技大学 信息科学与工程学院/人工智能学院

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中图分类号:

TP391.41

基金项目:

国家自然科学基金项目;湖北省教育厅科学技术研究项目


Mobile pedestrian tracking based on multi-camera information fusion
Author:
Affiliation:

1.School of Information Science \and Engineering, Wuhan University of Science \and Technology;2.School of Information Science and Engineering, Wuhan University of Science and Technology

Fund Project:

The National Natural Science Foundation of China;Hubei Provincial Department of Education research

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    摘要:

    针对室内环境下移动行人的跟踪问题,提出一种基于单目视觉和深度视觉传感信息融合的移动目标跟踪方法。首先利用单目视觉图像信息实现目标检测,并完成目标的语义分割;然后将单目视觉与深度视觉进行联合标定,通过深度相机获得识别目标的深度信息;最后,基于对目标跟踪实时性、准确性的需求,针对单目视觉P3P解算在目标较远时误差较大、深度相机识别帧率较慢的问题,设计了一种基于多相机信息融合的移动目标跟踪方法,将单目视觉获得的距离信息与深度视觉提取的深度信息进行了基于卡尔曼滤波的异步融合,实现对目标运动状态的准确估计。结果表明,该算法实现了对视野内行人目标位置实时检测和跟踪效果,具有高效的目标识别和跟踪能力。

    Abstract:

    For the tracking problem of moving pedestrians in indoor environment, this paper proposes a moving target tracking method based on the fusion of monocular vision and depth vision sensing information. Firstly, we use monocular vision image information to achieve target detection and complete semantic segmentation of the target. Then, we jointly calibrate monocular vision with depth vision to obtain the depth information of the recognition target by the depth camera. Finally, a moving target tracking method based on multi-camera information fusion is designed to address the problems of large error in monocular vision P3P solution when the target is far away and slow frame rate of depth camera recognition, and the distance information obtained from monocular vision and depth information extracted from depth vision are asynchronously fused to achieve accurate estimation of the target motion state. The results show that the algorithm achieves the effect of real-time detection and tracking of pedestrian target positions within the field of view, with efficient target recognition and tracking capabilities.

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历史
  • 收稿日期:2023-03-12
  • 最后修改日期:2023-06-22
  • 录用日期:2023-07-06
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