Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However...Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition （HHD） based object-motion oriented map （OOM）. By decomposing the image motion （optical flow） into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%-20% even over challenging scenes with complex background.展开更多
本文提出了一种基于深度学习的动态场景视觉里程计方法。使用轻量级Ghost模块与目标检测网络YOLOv5s结合构建C3Ghost模块,引入坐标注意力机制(coordinate attention, CA),在提高网络检测速度的同时保证检测准确性。并将其与运动一致性...本文提出了一种基于深度学习的动态场景视觉里程计方法。使用轻量级Ghost模块与目标检测网络YOLOv5s结合构建C3Ghost模块,引入坐标注意力机制(coordinate attention, CA),在提高网络检测速度的同时保证检测准确性。并将其与运动一致性算法结合,剔除动态特征点,仅利用静态特征点进行位姿估计。实验结果表明,与传统的ORB-SLAM3(orient FAST and rotated BRIEF-simultaneous localization and mapping 3)算法相比,在慕尼黑工业大学(technical university of Munich, TUM)RGB-D(RGB-depth)高动态数据集上绝对轨迹误差(absolute trajectory error, ATE)和相对位姿误差(relative pose error, RPE)平均有了90%以上的改善。相较于先进的同时定位与地图构建SLAM算法,也有相对提升。因此,该算法有效提升了视觉SLAM在动态环境下的稳定性和鲁棒性。展开更多
大量基于深度学习的无监督视频目标分割(Unsupervised video object segmentation,UVOS)算法存在模型参数量与计算量较大的问题,这显著限制了算法在实际中的应用.提出了基于运动引导的视频目标分割网络,在大幅降低模型参数量与计算量的...大量基于深度学习的无监督视频目标分割(Unsupervised video object segmentation,UVOS)算法存在模型参数量与计算量较大的问题,这显著限制了算法在实际中的应用.提出了基于运动引导的视频目标分割网络,在大幅降低模型参数量与计算量的同时,提升视频目标分割性能.整个模型由双流网络、运动引导模块、多尺度渐进融合模块三部分组成.具体地,首先,RGB图像与光流估计输入双流网络提取物体外观特征与运动特征;然后,运动引导模块通过局部注意力提取运动特征中的语义信息,用于引导外观特征学习丰富的语义信息;最后,多尺度渐进融合模块获取双流网络的各个阶段输出的特征,将深层特征渐进地融入浅层特征,最终提升边缘分割效果.在3个标准数据集上进行了大量评测,实验结果表明了该方法的优越性能.展开更多
为提高群体活动场景下细粒度人体姿态估计的准确率,优化网路中人体识别及姿态估计算法,在现有研究的基础上,提出一种结合多尺度预测以及改进并行注意力模块的多目标人体姿态估计算法。在充分利用不同尺度特征信息的基础上,实现高质量的...为提高群体活动场景下细粒度人体姿态估计的准确率,优化网路中人体识别及姿态估计算法,在现有研究的基础上,提出一种结合多尺度预测以及改进并行注意力模块的多目标人体姿态估计算法。在充分利用不同尺度特征信息的基础上,实现高质量的人体姿态估计;针对运动场景下多目标人体姿态数据集较少,提出一种数据集CUPB Sport Dataset。实验结果表明,该算法在公开基准数据集和自制数据集上分别达到了81.4 mAP和79.7 mAP,验证了该算法在运动场景下针对多目标的高效性。展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61503277.
文摘Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition （HHD） based object-motion oriented map （OOM）. By decomposing the image motion （optical flow） into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%-20% even over challenging scenes with complex background.
文摘本文提出了一种基于深度学习的动态场景视觉里程计方法。使用轻量级Ghost模块与目标检测网络YOLOv5s结合构建C3Ghost模块,引入坐标注意力机制(coordinate attention, CA),在提高网络检测速度的同时保证检测准确性。并将其与运动一致性算法结合,剔除动态特征点,仅利用静态特征点进行位姿估计。实验结果表明,与传统的ORB-SLAM3(orient FAST and rotated BRIEF-simultaneous localization and mapping 3)算法相比,在慕尼黑工业大学(technical university of Munich, TUM)RGB-D(RGB-depth)高动态数据集上绝对轨迹误差(absolute trajectory error, ATE)和相对位姿误差(relative pose error, RPE)平均有了90%以上的改善。相较于先进的同时定位与地图构建SLAM算法,也有相对提升。因此,该算法有效提升了视觉SLAM在动态环境下的稳定性和鲁棒性。
文摘大量基于深度学习的无监督视频目标分割(Unsupervised video object segmentation,UVOS)算法存在模型参数量与计算量较大的问题,这显著限制了算法在实际中的应用.提出了基于运动引导的视频目标分割网络,在大幅降低模型参数量与计算量的同时,提升视频目标分割性能.整个模型由双流网络、运动引导模块、多尺度渐进融合模块三部分组成.具体地,首先,RGB图像与光流估计输入双流网络提取物体外观特征与运动特征;然后,运动引导模块通过局部注意力提取运动特征中的语义信息,用于引导外观特征学习丰富的语义信息;最后,多尺度渐进融合模块获取双流网络的各个阶段输出的特征,将深层特征渐进地融入浅层特征,最终提升边缘分割效果.在3个标准数据集上进行了大量评测,实验结果表明了该方法的优越性能.
文摘为提高群体活动场景下细粒度人体姿态估计的准确率,优化网路中人体识别及姿态估计算法,在现有研究的基础上,提出一种结合多尺度预测以及改进并行注意力模块的多目标人体姿态估计算法。在充分利用不同尺度特征信息的基础上,实现高质量的人体姿态估计;针对运动场景下多目标人体姿态数据集较少,提出一种数据集CUPB Sport Dataset。实验结果表明,该算法在公开基准数据集和自制数据集上分别达到了81.4 mAP和79.7 mAP,验证了该算法在运动场景下针对多目标的高效性。
