For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are ac...For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.展开更多
提供了一个较大规模的基于RGB-D摄像机的人体复杂行为数据库DMV(Dynamic and multiview)action3D,从2个固定视角和一台移动机器人动态视角录制人体行为。数据库现有31个不同的行为类,包括日常行为、交互行为和异常行为类等三大类动作,...提供了一个较大规模的基于RGB-D摄像机的人体复杂行为数据库DMV(Dynamic and multiview)action3D,从2个固定视角和一台移动机器人动态视角录制人体行为。数据库现有31个不同的行为类,包括日常行为、交互行为和异常行为类等三大类动作,收集了超过620个行为视频约60万帧彩色图像和深度图像,为机器人寻找最佳视角提供了可供验证的数据库。为验证数据集的可靠性和实用性,本文采取4种方法进行人体行为识别,分别是基于关节点信息特征、基于卷积神经网络(Convolutional neural networks,CNN)和条件随机场(Conditional random field,CRF)结合的CRFasRNN方法提取的彩色图像HOG3D特征,然后采用支持向量机(Support vector machine,SVM)方法进行了人体行为识别;基于3维卷积网络(C3D)和3D密集连接残差网络提取时空特征,通过softmax层以预测动作标签。实验结果表明:DMV action3D人体行为数据库由于场景多变、动作复杂等特点,识别的难度也大幅增大。DMV action3D数据集对于研究真实环境下的人体行为具有较大的优势,为服务机器人识别真实环境下的人体行为提供了一个较佳的资源。展开更多
针对三维人体姿态估计的便捷性与准确性提升需求,提出一种基于TM-Net网络估计算法。该算法以MediaPipe为中心,融合帧率计算、动作检测、动作计数和真实坐标解析等多功能模块,实现对人体运动的精准检测与计数。针对公共数据集LSP(Leeds S...针对三维人体姿态估计的便捷性与准确性提升需求,提出一种基于TM-Net网络估计算法。该算法以MediaPipe为中心,融合帧率计算、动作检测、动作计数和真实坐标解析等多功能模块,实现对人体运动的精准检测与计数。针对公共数据集LSP(Leeds Sports Pose)和自建校园健身房运动数据集使用关键点的正确性概率(Probability of Correct Keypoint,PCK)、关节位置误差平均值(Mean Per Joint Position Error,MPJPE)和普罗克鲁斯对齐后的平均关节位置误差(Procrustes-Aligned Mean Per Joint Position Error,PA-MPJPE)等指标对该算法进行评估,并与目前先进的TP-3D网络估计算法进行对比。结果表明,TM-Net具有更高的准确率。此外,以开合跳为例进行消融实验,结果表明,TM-Net具有更强的泛化能力,能适应不同个体及拍摄角度的变化,满足了运动监测的实际需求。展开更多
基金supported by the National Natural Science Foundation of China (62173103)the Fundamental Research Funds for the Central Universities of China (3072022JC0402,3072022JC0403)。
文摘For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.
文摘针对三维人体姿态估计的便捷性与准确性提升需求,提出一种基于TM-Net网络估计算法。该算法以MediaPipe为中心,融合帧率计算、动作检测、动作计数和真实坐标解析等多功能模块,实现对人体运动的精准检测与计数。针对公共数据集LSP(Leeds Sports Pose)和自建校园健身房运动数据集使用关键点的正确性概率(Probability of Correct Keypoint,PCK)、关节位置误差平均值(Mean Per Joint Position Error,MPJPE)和普罗克鲁斯对齐后的平均关节位置误差(Procrustes-Aligned Mean Per Joint Position Error,PA-MPJPE)等指标对该算法进行评估,并与目前先进的TP-3D网络估计算法进行对比。结果表明,TM-Net具有更高的准确率。此外,以开合跳为例进行消融实验,结果表明,TM-Net具有更强的泛化能力,能适应不同个体及拍摄角度的变化,满足了运动监测的实际需求。
