A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models ...A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.展开更多
为了控制机器人完成复杂的多臂协作任务,提出了一种基于动态时间规整-高斯混合模型(Dynamic time warping-Gaussian mixture model,DTW-GMM)的机器人多机械臂多任务协同策略.首先,针对机器人示教时轨迹时间长短往往存在较大差异的问题,...为了控制机器人完成复杂的多臂协作任务,提出了一种基于动态时间规整-高斯混合模型(Dynamic time warping-Gaussian mixture model,DTW-GMM)的机器人多机械臂多任务协同策略.首先,针对机器人示教时轨迹时间长短往往存在较大差异的问题,采用动态时间规整方法来统一时间的变化;其次,基于动态时间规整的多机械臂示教轨迹,采用高斯混合模型对轨迹的特征进行提取,并以某一机械臂的位置空间矢量作为查询向量,基于高斯混合回归泛化输出其余机械臂的执行轨迹;最后,在Pepper仿人机器人平台上验证了所提出的多机械臂协同策略,基于DTW-GMM算法控制机器人完成了双臂协作搬运任务和汉字轨迹的书写任务.提出的基于DTW-GMM算法的多任务协同策略简单有效,可以利用反馈信息实时协调各机械臂的任务,在线生成平滑的协同轨迹,控制机器人完成复杂的协作操作.展开更多
基金Project(T201221207)supported by the Fundamental Research Fund for the Central Universities,ChinaProject(2012CB725301)supported by National Basic Research and Development Program,China
文摘A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.
文摘为了控制机器人完成复杂的多臂协作任务,提出了一种基于动态时间规整-高斯混合模型(Dynamic time warping-Gaussian mixture model,DTW-GMM)的机器人多机械臂多任务协同策略.首先,针对机器人示教时轨迹时间长短往往存在较大差异的问题,采用动态时间规整方法来统一时间的变化;其次,基于动态时间规整的多机械臂示教轨迹,采用高斯混合模型对轨迹的特征进行提取,并以某一机械臂的位置空间矢量作为查询向量,基于高斯混合回归泛化输出其余机械臂的执行轨迹;最后,在Pepper仿人机器人平台上验证了所提出的多机械臂协同策略,基于DTW-GMM算法控制机器人完成了双臂协作搬运任务和汉字轨迹的书写任务.提出的基于DTW-GMM算法的多任务协同策略简单有效,可以利用反馈信息实时协调各机械臂的任务,在线生成平滑的协同轨迹,控制机器人完成复杂的协作操作.
