| 基于相机模型投影的多目标三维人体跟踪算法 |
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| 引用本文： | 李柯江,黄林,牛新征. 基于相机模型投影的多目标三维人体跟踪算法[J]. 计算机应用与软件, 2022, 39(1): 168-175. DOI: 10.3969/j.issn.1000-386x.2022.01.025 |
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| 作者姓名： | 李柯江 黄林 牛新征 |
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| 作者单位： | 电子科技大学信息与软件工程学院 四川 成都 610054;国网四川省电力公司信息通信公司 四川 成都 6100413(电子科技大学计算机科学与工程学院 四川 成都 611731 |
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| 基金项目： | 教育部联合基金项目;国网四川省电力公司信息通信公司项目(SGSCXT00XGJS1800219)。 |
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| 摘 要： | 针对基于二维目标检测和卡尔曼滤波的多目标人体跟踪算法在视频拍摄角度不定的情况下,检测算法生成不同角度人体二维检测框的朝向和尺度混淆以及卡尔曼滤波器随机初始化造成的初始跟踪误差逐步放大问题,提出一种基于相机模型投影的多目标三维人体跟踪算法.在人体检测阶段,提出Multi-task RCNN(MTRC-NN)网络,使用人体...
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| 关 键 词： | 相机模型 深度学习 多目标跟踪 卡尔曼滤波 三维目标检测 |
MULTI-OBJECT 3D HUMAN TRACKING BASED ON CAMERA MODEL PROJECTION |
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| Li Kejiang,Huang Lin,Niu Xinzheng. MULTI-OBJECT 3D HUMAN TRACKING BASED ON CAMERA MODEL PROJECTION[J]. Computer Applications and Software, 2022, 39(1): 168-175. DOI: 10.3969/j.issn.1000-386x.2022.01.025 |
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| Authors: | Li Kejiang Huang Lin Niu Xinzheng |
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| Affiliation: | (School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,Sichuan,China;State Grid Sichuan Electric Power Company Information and Communication Corporation,Chengdu 610041,Sichuan,China;School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China) |
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| Abstract: | Concerning the problem of the 2D bounding box scale and orientation confusion generated by the detection algorithm when detecting the human body from different angles,and the gradual amplification of initial tracking error caused by random initialization of Kalman filter in multi-object human tracking algorithm based on 2D object detection and Kalman filtering,a new multi-object 3D human tracking algorithm based on camera model projection is proposed.In the human detection phase,3D target detection guided by human movement trend replaced the traditional 2D target detection through Multi-task RCNN(MTRCNN).The detected human bounding box was modified by the camera model and projected into the world coordinate system.In the tracking phase,the Kalman filter parameters were initialized by the target’s 3D scale and orientation information.Then 3D Box IOU,movement trend feature and appearance feature were combined to generate a target matching score,and the data was correlated through the Kuhn-Munkres(KM)algorithm.Compared with several algorithms on annotated dataset and MOT17 dataset,the proposed algorithm has more stable initial tracking performance and less ID Switch. |
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| Keywords: | Camera model Deep learning Multi-object tracking Kalman filtering 3D object detection |
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